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Margaret Lutz: Everyone welcome back.

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Margaret Lutz: you're able to get your coffee or whatever it is that you needed and so next we have as James said me for some presentations and then discussion on the recent results of the GD X and monitored vertical groups.

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Margaret Lutz: Where we even saw maybe a little bit of an excess maybe not we can talk about it.

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Margaret Lutz: isn't it exist, but what we want to make of it, and so the first speaker up is an.

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Ann Miao Wang: eye.

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Ann Miao Wang: yeah can you.

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Ann Miao Wang: Yes, Okay, let me just share.

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Margaret Lutz: Okay yeah I can see your screen.

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Ann Miao Wang: Okay, can you see full screen oh.

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Margaret Lutz: I can see full screen hopefully it will move forward.

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Ann Miao Wang: Okay, so i'll just start if that's Okay, yes.

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Ann Miao Wang: Great.

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Ann Miao Wang: So hi everyone i'll be presenting highlights from the pixel dx analysis, this is what the full run to data set.

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Ann Miao Wang: So this is a search for heavy Long live charged particles with large ionization energy loss using the Atlas experiment.

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Ann Miao Wang: And excitingly our paper was recently submitted to Jay hub, and here is the archive link.

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Ann Miao Wang: OK, so the basic strategy of this analysis is that it relies on the fact that ionization energy loss or dx of a charged particle traversing any material, it depends on its Lawrence beta gamma.

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Ann Miao Wang: So we can use this property to.

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Ann Miao Wang: Look, for massive long list particles, and this is because they will travel more slowly than light standard model particles through our detector if we fix.

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Ann Miao Wang: The momentum so if we have a mental requirement so according to the beta Blocker relationship which is depicted in this plot on the left here, so this is dx on the y axis.

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Ann Miao Wang: And beta gamma on the X axis.

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Ann Miao Wang: slower particles will live to the left of the curve in the US have larger measure dx so we can exploit this property to find evidence for new physics, by looking for a track with large GD X, which also has high PT and as high quality.

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Ann Miao Wang: Okay, so because the beta block relationship that I just showed you governance all charged particles this analysis is really relatively model independent we have broad sensitivity to charge long NASA particles with lifetimes of order nanosecond to stable.

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Ann Miao Wang: But we are specifically in this analysis interpreting on supersymmetry and what the full run to data set we're targeting Long live leno's charging US can swap don's.

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Ann Miao Wang: Note that the winos here we're actually looking for our hydrants they had an ice to travel to do chapter, as these composite particles.

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Ann Miao Wang: And then we have leno's slept ons in charge, he knows which sort of span the mass range, not a really broad mass range and.

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Ann Miao Wang: variety of different cross sections, so when we're designing this analysis, we have the philosophy again to be as independent as possible, and we have to remain flexible and sensitive to a large range of masses and lifetimes.

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Ann Miao Wang: Okay So how do we actually do this well in order to look for a highly ionizing high PT track we rely largely on the Atlas inner tracker, this is a cartoon of the inner tracker here.

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Ann Miao Wang: And we use the full enter detector to measure the momentum the transverse momentum and the fourth innermost layer he innermost layers here are called the pixel system.

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Ann Miao Wang: And we use these specifically to measure the dx for layer

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Ann Miao Wang: One special thing about this is that the innermost layer of the pixel system is called the insert of will be layer or the ipl, and this has different front end electronics, with respect to the rest of the pixel system.

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Ann Miao Wang: It has an overflow bit in the event of sufficient charge deposition which helps us further tag highly ionizing particles.

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Ann Miao Wang: One extra thing also is that the we get a dx pit per layer and this these hits are these clusters actually sample a Lambda back distribution.

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Ann Miao Wang: So, in order to convert this to layer dd axes and to attract it yet what we do is we apply a truncated mean algorithm.

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Ann Miao Wang: So, in order to do this, what we do is for each track we order the DVD X clusters by charge we throw out a subset of the largest hits and then we average the rest and what we're trying to track here is the MTV or the most probable value of the dx measurement.

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Ann Miao Wang: Okay, so now we have our track the dx and our track the team and then one particularly nice aspect of this analysis is that with those things that we can reconstruct the mass of any track that we see.

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Ann Miao Wang: So am equals P over beta gamma and GD X probes beta gamma so we get that sort of for free.

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Ann Miao Wang: Right so.

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Ann Miao Wang: Using the dx measurement, however, requires a ton of custom work done by the team specific to this analysis, so we have a unique set of calibrations and treatments of the dds rainbow.

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Ann Miao Wang: So, for example, if you look at this plot on the upper left you see dd acts as a function of deliberate integrated luminosity and you also see three slices of Ada so.

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Ann Miao Wang: probing different regions of the detector and you see a very significant decrease of dd access time passes, and you also see that, through different data slices that dds measured is different, so we correct for these things on a run by running basis and for these detector effects.

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Ann Miao Wang: We also see a discrepancy of dds modeling in atlas Monte Carlo compared to data, so this is illustrated on the plot in the lower left.

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Ann Miao Wang: This is long dx here split into tracks with and without an overflow in the IBM so that's represented by overflow zero and overflow one.

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Ann Miao Wang: And we see, for example, one discrepancy is very apparent in the tails so for our signal Monte Carlo we actually use a data driven dds template to replace the GD X values here.

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Ann Miao Wang: And then, finally, maybe the crux of our analysis relies on the CDS to beta gamma calibration so instead of using.

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Ann Miao Wang: Anything analytic or the beta Blocker formula we derive our own calibration and the example platform a calibration is on this.

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Ann Miao Wang: Is this plot, here we have dx as a function of beta gamma and these points all come from dx as measured by standard particles in slices of momentum using a special very special do so.

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Ann Miao Wang: Okay, so we've done our dds corrections and calibrations, so now we can move on to our event selection, so this is our event selection and track selection in a snapshot we trigger on high missing, etc, and we require that our events pass and offline missing at cut above 170 gv.

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Ann Miao Wang: Then, after that we look for a high momentum track that central and architecture and has a GD X greater than 1.8.

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Ann Miao Wang: We also impose a series of very important track quality requirements and vetoes to reduce standard model background.

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Ann Miao Wang: And these details can be found in the paper or in the backup.

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Ann Miao Wang: Finally, after our track passes are selection, we put it into a group called so inclusive.

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Ann Miao Wang: And then we can categorize the track into six exclusive signal bins according to hit pattern GD X value and new and information.

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Ann Miao Wang: And so we call these regions are exclusion regions and their design and they're more powerful than the region so i'll talk about next for excluding specific models.

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Ann Miao Wang: So we also have a set of more inclusive regions where instead we just sub divide by the X value so dds between 1.8 and 2.4 or 2.4 and greater for inclusive low and inclusive hi.

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Ann Miao Wang: These are our last model independent model dependent they're easier for reinterpretation and we call these are discovery regions, the some of the discovery regions is exactly equivalent to the some of the exclusion regions and that's the Sri inclusive.

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Ann Miao Wang: So after applying her about the selection our final signal region plot that we make is the mass distribution of any candidate tracks.

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Ann Miao Wang: So what we need to do when we estimate our background is accurately predict the mass of our expected background in the signal region.

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Ann Miao Wang: Our background can consist of any standard model particle which will leave an ice will isolate attract and then combine that with statistical fluctuations in dx us following that land our distribution that I showed you earlier.

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Ann Miao Wang: And then these tail events can pass and we can get a track in our signal region, so this is extremely difficult to model in Monte Carlo so we use a completely data driven technique designed to predict the expected mass distribution in the signal region.

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Ann Miao Wang: And in order to do this, what we do is we defined to control regions adjacent to the signal region in phase space, so the kinematic control region, which is that low dx and the dx control region, which is that low missing at are met.

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Ann Miao Wang: We randomly draw momentum and dx from the distributions can these regions, which we expect our representative of the distributions in this region.

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Ann Miao Wang: And we combine this P or momentum and dx sample to define a toy truck and because we are P amp D dx we can calculate the mass of this toy track and then repeat this order 10 million times.

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Ann Miao Wang: Finally, after creating the expected mass distribution through this method we normalize the distribution to a low mass region and data and we apply our dx cut and now we have a background distribution background mass distribution in our signal region.

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Ann Miao Wang: Okay, so to give us confidence in the background estimation, we check the background estimation, in two sets of validation regions, so one is to find that low PT relative to the signal region and want us to find out hide.

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Ann Miao Wang: So we check the predicted and observe mass distributions you can find these in the backup these plots are just.

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Ann Miao Wang: Comparing the observed yield and the expected yield in the district in these regions so each region is subdivided using track and IV all information and also.

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Ann Miao Wang: Add X information in a manner analogous to the single region, and you see here.

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Ann Miao Wang: The Green represents the systematic uncertainty that we see a good agreement between observed and predictive yields within the background uncertainty, so this gives us confidence in her background estimation method.

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Ann Miao Wang: Okay, so finally here are the results, these are the predicted and observed mass distributions in the to discovery regions inclusive low on the left and inclusive high on the right, so the predicted distribution is in the dark blue with uncertainty in the shaded purple.

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Ann Miao Wang: And the observed data is represented by the black points and then there's a few signal samples overland which gives you sort of sense of our mass resolution as a function on mass and, as you can see, in Sri inclusive low the data and the predicted distributions agree extremely well.

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Ann Miao Wang: In the inclusive high region.

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Ann Miao Wang: agree is very well at low mass, but we do see an excess of observed tracks at high mass here.

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Ann Miao Wang: Okay So how do we quantify this access before and blinding what we actually do is we define a set of mass windows, in which we count cut and count the events so many signal bins basically these mass windows are optimized to each target particle mass and lifetime.

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Ann Miao Wang: The mass window definitions for long lifetimes is shown in the upper right plot, you can see the extent of the mass windows, which is represented by this purple line.

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Ann Miao Wang: And after unblinding we calculate the p value and each mass window, so the p values for us are inclusive high for these different.

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Ann Miao Wang: Mass windows sub divided between short and long lifetimes is shown here, and you see that this this one with the region with the smallest P value that spans from.

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Ann Miao Wang: TV and us are inclusive high that's the highest deviation and we see seven events and expect 0.7 about us here, plus or minus zero point for the significance of this is 3.6 and when you account for the customer effect it's the point very similar.

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Ann Miao Wang: Okay, so let's examine these access events, a little bit more so here's a plot of the data and and the expected background in our asr inclusive region so including low and high.

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Ann Miao Wang: d dx is on the y axis momentum is on the X axis the blue represents our expected background, distribution and the red points are data.

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Ann Miao Wang: Here in the box are the seven XX trucks and Just to give you a sense of the topology of these events, six out of seven have a jet back to back with the signal track five out of seven or match to me once and three out of seven have two new ones in the event.

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Ann Miao Wang: So these tracks were also systematically examine for evidence of detector effects such as an almost pixel clusters for isolation things like pile of other systematic effects and non were found.

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Ann Miao Wang: We did do an extra cross check.

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Ann Miao Wang: So we looked at the available kilometer immune system timing information so time of flight measurements here.

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Ann Miao Wang: And evidence of these tracks coming from slower particles, as suggested by their large GD X was not confirmed, so these time of flight measurements were very consistent with beta of one so speed of light.

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Ann Miao Wang: Okay, so with observed data, even with access we can still set limits on the CD models that I previously mentioned so here are the limits for gleaners charging nose and styles in a clockwise fashion.

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Ann Miao Wang: We calculate the limits here, using toy experiments and we use the exclusion regions and so discovery regions, so those finer been regions that I mentioned, and we do a multi benefit over all of them.

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Ann Miao Wang: So we end up excluding who knows, for example, stable going knows around up to 2.1 TV and Meta stable to a maximum around 2.3 TV.

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Ann Miao Wang: Charging those are excluded, up to around 1.05 TV for the middle lifetime, the 30 nanosecond lifetime and styles are very difficult to search for, but we do manage to exclude this parameter space between around 200 and 360 GB at top 10 nanoseconds.

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Ann Miao Wang: Okay that's it so In summary, on new search was conducted the pixel dx analysis to look for having long live charged particles and proton proton collisions.

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Ann Miao Wang: Many analysis improvements were made from the previous search from 36 in respective rooms, these are just a few but we made improvements to the dds calibration and modeling the data driven template we employed a higher dx special.

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Ann Miao Wang: The Ibo overflow categorization information is new so that's really helps us discriminate signal versus background.

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Ann Miao Wang: We do a multiplayer and fit this time and we also improved our track quality cuts we added a new validation region and a more complete systematic uncertainty estimation, to give us more confidence in our background estimation.

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Ann Miao Wang: We set competitive woman's on glee no charge, you know and style models and we did see an excess and cross checks were conducted, but the existence of slow particle suggested by the access was not confirmed using time of flight measurements from the Milan and Cameron Bure systems.

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Ann Miao Wang: yeah that's it.

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Ann Miao Wang: Are there any questions.

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Margaret Lutz: Okay, and um thanks a lot, in fact, we will have to discussion sections kind of coming up soon um one on the experimental aspects of the search and.

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Margaret Lutz: Multi touch vertical search, which is the next presentation and then another one after the presentation and then Daniela.

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Margaret Lutz: On the phenomenal logical aspects of this, and so, if you have a question that's you want to ask before we then talked about multi touch particles and the overlap those two searches.

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Margaret Lutz: And then, can you keep your hand up now, and if you think will be more appropriate for the discussion sections that we have later, can you put it down for the moment, and then we can, maybe, and this the sort to see which is the right time and.

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Margaret Lutz: Okay, so.

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Margaret Lutz: Michael.

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Michael Albrow: Thank you for agency obviously i'm not sure.

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Michael Albrow: It seems to assume that the particles have charged one and, as you know that there are also like nuclei produced NPP conditions even helium three and him for maybe have had few.

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Michael Albrow: Consider the well one one question is this analysis could actually show light nuclei been produced a short, that this is not an issue here.

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Margaret Lutz: And certainly Michael is, in fact, up until I was 10 make this before, is that we are right now and, right after this have a presentation on multi touch particles which look at.

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Margaret Lutz: Heavy touch particles with her does from two to seven, which is why we stuck things in this way, so afterwards we'll have a discussion section on kind of the overlap of these analyses.

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Margaret Lutz: and exciting.

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Margaret Lutz: Now, if people want to ask questions that are just specifically about the dx that they think won't involve also the presentation that will be in a few minutes.

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Michael Albrow: Okay, thank you.

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Thanks.

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Margaret Lutz: And matt do you think your question is better for now.

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Matt Strassler: Yes, I think, so this has to do with the issue of the cross checks that you made on on the tracks.

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Matt Strassler: That you mentioned that you checked for certain quality things I don't know if you put that slide back up quickly but.

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Matt Strassler: Those particular tests do they usually fail when you're looking at control region tracks that have a an exceptionally high value of the dx way out on the tail.

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Ann Miao Wang: So when you sit cross checks, do you specifically are you.

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Matt Strassler: don't I don't mean the beta test, I mean all the others.

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Matt Strassler: You can go back to that slide yeah the pixel clusters, the last letter and all those things do they just do those tests tend to fail, when you have a control region track with a large amount of tea dx.

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Ann Miao Wang: yeah.

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Ann Miao Wang: that's a good.

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Ann Miao Wang: i'm not sure.

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Ann Miao Wang: If we've specifically.

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Ann Miao Wang: checked for that, so I do know that, for example, when we invert a lot of our track quality cuts so things like split insured hits in isolation, we actually get very, very few.

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Ann Miao Wang: Control region events per cut which pass or additionally pass into our control region, so I think that our quality cuts here during a really, really good job.

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Matt Strassler: OK thanks.

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Margaret Lutz: OK thanks matt and then.

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Margaret Lutz: I think we can.

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Margaret Lutz: have one two more questions now and.

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Margaret Lutz: Maybe cigarettes for after the next step, and considering the time so i'm young, do you think your question is less now.

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and

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Jan Heisig: yeah, I think, so my question is just on slide 12 you show the these limits and for the glue no, in particular, they are much stronger or.

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Jan Heisig: Significantly amount stronger for the metal stable case then for.

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Jan Heisig: The detector stable case and i've seen this in various of analysis will see this seems to be a common thing that you come and why I mean because I found from just the D ED X I wouldn't think that the detector stable would be less sensitive, could you comment on that.

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Ann Miao Wang: yeah, it has to do with our other concerns mainly or a trigger and are missing the tickets so, for example, if you have a Meta stable signal you'll be able to see it stuck a products in the missing at the online missing teeth, which is what we trigger on.

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Jan Heisig: Oh.

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Ann Miao Wang: Okay yeah well for cable the civil tracks that you don't see those tracks, so they live, very little missing or energy in the calendar so it's difficult to.

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Jan Heisig: Alright, so this really comes from a trigger.

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Jan Heisig: yeah Okay, thank you, thank you.

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Jan Heisig: Okay.

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Jan Heisig: Thank you and.

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Margaret Lutz: So, Christopher.

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Christopher Hill: hi my question is about the in the use of the average quantity to to make a the the X metric you actually lose some information because the the probability to have a large dx deposit is not at not uniform radio Lee in general, in general, it might be for an ideal track, but but.

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Christopher Hill: You know you have a much higher probability of having that large GD X on maybe the first layer or something so do you look at the you look at the for those seven tracks, you have the.

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Christopher Hill: Have you unfolded that average and and can show the the the charge depositions per layer

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Ann Miao Wang: And we have looked at the church oppositional PR layer I and it doesn't look to be problematic if that's what you're asking.

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Ann Miao Wang: and actually the truncated mean algorithm is a really robust algorithm it's it's very powerful and I know there have been studies using, for example, a fit instead without instead of throwing information but it's it's pretty comparable to methods such as that.

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Christopher Hill: But it does make the assumption that the each layer is equally likely.

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Christopher Hill: To produce a high that you know that that all layers are following the same line, though it's sort of implicit in that, but Okay, if you looked at it would be helpful to publish that information or if that list has looked at it.

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Ann Miao Wang: Because so so most layers I mean it depends on the thickness of course, but it would be similar measurement because the beta gamma doesn't change as you pass through the layers.

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Christopher Hill: yeah that's what I meant by it, an ideal ideal track, but when you have when you have tracks that.

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Christopher Hill: That are not really you know, for example, you pick up a hit from pile up in the first layer and falsely attached that but not so so much that.

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Christopher Hill: It for that pulls the Chi square is such that the track is rejected by your track quality costs, I mean this is something we've seen in other experiments before where we're and current where you you, you can pick up.

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Christopher Hill: it's it's just to assume it's a perfect track and then everything is.

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Christopher Hill: Equally, you got all the if you could compensate for this with the track quality cut so it's similar to the question from matt but I didn't think that you really answered that either, and maybe you haven't answered my question either, but.

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Leonardo Rossi: But the first layer is a mountain peaks and so the ability of.

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Leonardo Rossi: Saving in the track together is it's not the high end for the other layers.

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Christopher Hill: But at first, the first layer is this Ibo layer with the.

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Christopher Hill: yeah so yeah be interesting to hear a little bit more how that information is used, but maybe Maybe my comment would apply to the second of the first layer or something the second layer then but it because you don't have is it a.

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Christopher Hill: What kind of read out of you have out of the idea and it's a limited the limited range right.

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yeah.

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Christopher Hill: So you just rely mostly on this overflow bit for that that one so that's not going to give you too much information, but if.

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Christopher Hill: For example, you said you looked at this distribution that wasn't anomalous like they all have high hits on the on the ipl or Is that how you I mean I guess you don't have the information handy but but they all follow expected distributions.

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Ann Miao Wang: yeah I would say they don't deviate from expected distributions and then they're not all tracks with IBM hits if that's where you're at as well.

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Ann Miao Wang: And in regards to track quality, I mean you check things like pile up in isolation, so they don't look like what you're suggesting with pile up.

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Christopher Hill: Okay, thank you.

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Margaret Lutz: Okay, thanks, Mr I think say and then alien boundless maybe we can save these questions for this customer section that we have coming up pretty soon and and maybe for the moment, we can move on to your presentation on the very simple results on.

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Yury Smirnov: Heavy multitiered particles yes hi so i'm going to share the slides now.

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Yury Smirnov: I think you can see the right.

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Yury Smirnov: Okay okay great so.

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Yury Smirnov: This is.

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Yury Smirnov: A search for Haiti, along with most church particles in the forum to data.

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Yury Smirnov: With arrows detector.

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Yury Smirnov: We are searching for human like particles with file a few charges from two to seven, based on the organization losses in.

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Yury Smirnov: Three years three actually it'll sound detectors that this is generally a blue sky search by the rest of models that in fact predict new particles.

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Yury Smirnov: Was surcharges this is almost Community geometry model, the walking technicolor model and the left, right symmetric model.

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Yury Smirnov: and any observation of such particles would be an evidence of physics be honest and model.

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Yury Smirnov: And for the single samples more co Monte Carlo sam's we use particle players with mess of from 500 GB to do.

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Yury Smirnov: 20 hundred GB with a step of 300 GB and charges 23456 and seven previously or the trail young and for confusion mechanisms, we do not have a paper yet, at least not the public one, but we have a cough note and the blots and tables from the from that continent which is publicly.

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Yury Smirnov: First, the production modes we use the this to production modes the the phone fusion mode was never used in previous mcp searches in atlas and for the drill Yun mode only the fordham mediator production was used in previous mcp searches, so the Z mediator is near here to.

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Yury Smirnov: Now for this selection we use a derivation selection, which keeps all events from the mainstream, with at least one flying combined noon with BT greater than 50 GB.

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Yury Smirnov: Then we use three trigger requirements or three three or trigger requirements is a single your trigger.

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Yury Smirnov: me see to trigger the soho lately on trigger this is new one, this is used again in this kind of search for the first time.

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Yury Smirnov: They seemed on your triggers the image matrix trigger here reverse the mcp is armed you like.

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Yury Smirnov: And the largest in Nice originates from the initial state radiation jets recoiling have a baby bear and the you lake new trailer also.

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Yury Smirnov: known as the out of time, your trigger it fires and events with a BT greater than 50 gv jack in the current batch processing.

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Yury Smirnov: And a software immune in the next bunch of your awesome and it is scaled and it was brought into service in the 2017 data, taking as one of the algorithms or the you know, on top of trigger.

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Yury Smirnov: Now for the selection, we are selecting events with at least one combined you and with at least medium quality.

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Yury Smirnov: Of the transverse momentum measured only by the system, greater than 50 GB the overall transverse momentum measured by the combination of inner detector immune system is greater than 10 g.

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Yury Smirnov: et is limited to 2.0 this is dirty limitation and we also require reliable dds estimation of MBT TRT and and the pixel by pixel is all for George to because of the church situation and i'm going to talk about this a bit later.

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Yury Smirnov: than me owner for them to reconstruct, it has to provide the transverse momentum or particle after it has lost its energy in the Korean or so we rely on the standard reconstruction algorithm and we do not doing anything fancy like you go from here or.

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Yury Smirnov: An algorithm reconstructing slow meals.

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Yury Smirnov: And we also require the the corresponding ID track segments should be isolated from other it tracks, this is in order to limited background contribution from two or more tracks firing.

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Yury Smirnov: The same two artists rose or entities, they stable features, the entire pre selection criteria or the entire set of the pre selection criteria.

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Yury Smirnov: Now for the.

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Yury Smirnov: discriminated quantities, we have big zodiacs as an already mentioned it's based on the measurements of an output signal with.

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Yury Smirnov: From this from the discriminate or of every pixel, then we have the tr TD X, which is based on measurements of a signal with exceeding the lower threshold and divided by the track segment length entirety.

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Yury Smirnov: Then there is a high production sorry, there is a fraction of potential hits and TRT with a single amplitude overtly 60 each.

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Yury Smirnov: On a truck segment and dirty again and, finally, there is a urine system dx entity is called the empty X, this is.

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Yury Smirnov: Based on the measurements over time interval when the single amplitude from the amplifier shape or discriminated in MBT exceeds a certain threshold within the first nanoseconds of that signal.

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Yury Smirnov: And the aurora dx variables from all these three sub detectors they have always their own arbitrary values.

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Yury Smirnov: are arbitrary units, and so we define the significance as a difference between the observed dx and have a particle and the one expected from immune from CD came in.

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Yury Smirnov: Excuse me in data in units of.

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Yury Smirnov: measurement here's the formula.

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Yury Smirnov: So for the test election we divided for charge to was on the left.

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Yury Smirnov: This pixel the significance on the left, you see them humans from CD case in data Monte Carlo and on the right, this is the signal, and this is for church and for the file charges big zodiac saturates, and we cannot discriminate between can hardly discriminate between.

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Yury Smirnov: background and signal, so we do not have type selection criteria criterion for this market research.

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Yury Smirnov: Now for the.

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Yury Smirnov: For the final selection criteria we use the dirty significance top left the charity heiser show his fraction top right and the mdt significance on the bottom so everywhere, you see him he owns and some signal.

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Yury Smirnov: Now for the final selection we use the OECD planes, as shown here on the left, this is charged to search and on the writers charges from three to seven.

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Yury Smirnov: So, for the first category, we use the meds significance for strategic significance in brave this is data and the region is the single region and.

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Yury Smirnov: Here blue and.

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Yury Smirnov: Red is a single month.

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Yury Smirnov: And for the others surcharges this is NDTV Eric significant source of sky searchable tier two hits fraction.

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Yury Smirnov: Again grey is data these the single region and yellow and blues singer Monica for different churches and simple would be time CRA gives expectation of 1.5 plus minus 0.5 expected and Cindy here and all point oh three events in you know this region now for the.

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Yury Smirnov: signal efficiency, so these blocks feature the signal efficiency versus mass for each charge.

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Yury Smirnov: And versus charge for each class, and it is defined as a fraction of Mount Carmel events with at least one mcp in the region.

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Yury Smirnov: That is, after pricing all the selections among all generated events, and there are several reasons for all the rotation so here, you see that the largest ones about 47% for the low masses.

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Yury Smirnov: The efficiency drowsy mystically because of the scissor ability and especially PT or charger requirements for high messy drops because of limited.

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Yury Smirnov: Construction efficiency of meals and for high charges, this is especially observed here the supplies below.

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Yury Smirnov: Because we have decreased in the efficiency, because of large organization last slow sparkles down and they may not make it into the tiny window anymore, or may lose all the kinetic energy before the immune system also.

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Yury Smirnov: Obviously, there is a stricter effective PT overcharged requirement and a large delta electric yield distorts timing parameters of entity hits from mcps leading to a smaller number of for constructed combined aeons.

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Yury Smirnov: For the uncertainty on the background estimation, we use so called mask region method.

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Yury Smirnov: We introduce the master agents between ABC and D amp D or regions, this is showing you this picture of the shaded areas, the mask region.

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Yury Smirnov: And some and also between the a plus B and C plus deeper, this is not shown and the background estimation.

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Yury Smirnov: Will calculate it without accounting for the interest in style inside this mask regions and the systematic uncertainty is the relative difference between the nominal background estimation, and then you one and I will repeat it, for more than more than 20.

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Yury Smirnov: different definitions of masks or agents and take the maximum relative difference as a final systematic uncertainty with this is 33% for judge two and 12% for grief shoulders.

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Yury Smirnov: Now, what we have observed.

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Yury Smirnov: In the single region for church do search we expected 1.5 plus minus Point five plus minus Point five events and observed for.

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Yury Smirnov: This is a small excess of 1.5 Sigma and P value is 6% and fortune just great to have them to we're expected or point oh three.

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Yury Smirnov: Plus minus some something small and did not observe anything and so these blood shows the unblinded a B, C D playing for charge to you can see here and also use isn't inversions here, where are these four events, it so.

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Yury Smirnov: They sit very, very close to the boundaries between the single region and non signal once.

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Yury Smirnov: Now for the limit stating we make use of the CLS method, it takes the luminosity uncertainty statistical and systematic uncertainty expected background estimation uncertainties and signal efficiencies as a signal leakages as uses parameters this blood shows the.

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Yury Smirnov: The cross section limits versus mass and also the theoretical cross sections, and this is the mass movements versus charge it both expected them to observe.

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Yury Smirnov: Now, and now a very interesting question would be whether we whether these four events are the same.

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Yury Smirnov: As observed in the talk in the paper that and just talked about or worthies actually completely different events, the answer is, they are completely different.

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Yury Smirnov: And on this slide I show the same candidates that and just talked about, and the exact reasons why we did not observe them in our single region so i'm going to go from the from the beginning, so there are three groups here three three groups of.

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Yury Smirnov: The seven candidates can be divided in three groups, the first two candidates are indeed combined meals, with high peaks odd X values, as shown here they both pass our big zodiac significance gut.

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Yury Smirnov: But they feature very low low none yeah low low mdt dx and TRT experiments as shown here, and here I I lot where these two candidates sit on our church to ABC plane, so they both are in the region, which was completely Bagram, dominated.

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Yury Smirnov: The next two candidates number three and four, they are not yields, but they do feature high tr TD significance, that is, they would pass out 30 X requirement if they weren't given so of course and also they have really high 35 social hits production and also fairly high big zodiacs various.

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Yury Smirnov: Our God is it 13 and the first one does not does not pass this card, but the second one does and they're asked.

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Yury Smirnov: The remaining three candidates, they are combined deals, but neither of PICs or the significance naughty or direct significance nor mttf significance are high enough to.

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Yury Smirnov: buy us our selection right here, as can be shown and this this table cells, so this a conclusion we performed the search for along with mal charged particles in the foreground to date in atlas.

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Yury Smirnov: The changes with respect to the previous search or the 2015 2016 data are related to the improvements in the production model and to the user chillin additional trigger.

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Yury Smirnov: We use for ionization estimators from three and subsystems two separate signaling background the signal efficiencies up to 47% is most mass and church three.

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Yury Smirnov: We expected 1.5 plus minus Point five plus minus Point five moments for the church to category and observed for and did not observe anything for the church greater than two categories.

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Yury Smirnov: All for observed events here are very close to the boundaries between the signal and lasting our agents and they say observational is within two standard deviation from the expectation, so we do not call with the discovering the conf note.

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Yury Smirnov: and

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Yury Smirnov: The observed mess limits range from 500 G up to 1015 or 1600 gv depending on the charge and the largest increase in the desert mess lemons with respect to the previous version is 450 GD this torture seven and the manual check of the seven candidates that.

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Yury Smirnov: The CEOs of big zodiacs analysis observed.

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Yury Smirnov: It explains the exact reasons for their absence in our second region.

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Yury Smirnov: Think, thank you.

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Margaret Lutz: Thanks so much.

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Margaret Lutz: For that presentation, so I guess.

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Margaret Lutz: Yes, um does anyone have any clarification questions or questions very specific to Uri before we have discussion ever the experimental aspects of these two dx and mcp searches combined.

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Margaret Lutz: And elite yeah Leonardo do some.

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Leonardo Rossi: Yes, yes, a one question about the show that out of the seven events that two events which are not me one, so they have high tr TD dx and idea at a fraction of.

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Leonardo Rossi: Say hi eats.

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Yury Smirnov: Yes, yes, yes.

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Leonardo Rossi: So he you know how likely, this is for another mcculloch.

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Yury Smirnov: This is very unlikely, especially the second the ganas number for this is all point for integrity heiser hates fractions is very unlikely.

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Okay.

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Leonardo Rossi: So is that what you what you did not mention is that.

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Leonardo Rossi: Part of the the essentially that the the threshold, on which we observe that they need the X access is larger than 2.4 the threshold at which wich your CAP is larger than 3.1 so that is inserted into seven events, some of them, they just cannot be there.

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Leonardo Rossi: Yes, okay so.

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Yury Smirnov: yeah like like number three, for example.

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Leonardo Rossi: Yes, number three and the roster the last three events.

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Leonardo Rossi: Yes, so essentially.

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Yes, okay.

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Leonardo Rossi: Okay, no that's all they wanted just to know the likelihood of this very little bit surprising happening.

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Leonardo Rossi: With the two tracks in terms of view on.

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Margaret Lutz: It and how do you have a specific question for free switch slides.

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Todd Adams (he/him/his): yeah I did on slide six you you give the significance formula, and you have Sigma in the denominator Are you assuming a gaussian Sigma lab or how.

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Yury Smirnov: So yeah yeah we're assuming the ghosts and Sigma.

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Todd Adams (he/him/his): Black a good assumption.

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Yury Smirnov: yeah it's a pretty good assumption that because we actually finished the course of the distributions to get that and it the course follow the ghost and distribution perfectly.

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Todd Adams (he/him/his): The core does, but there are details to it right.

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Yury Smirnov: Right right but we don't really care about the details here.

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Yury Smirnov: Because of all worked all we need is some some normalization of the significance and this is, this is all we need.

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Todd Adams (he/him/his): Alright, thank you i'm gonna have to think about that.

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Margaret Lutz: Okay, thanks time and.

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Leonardo Rossi: Never question about.

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Leonardo Rossi: I wanted to ask you about the dynamic range of the tr TD dx and then bta eds can you really measure 49 MIPS.

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said.

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Leonardo Rossi: Are you sure of this.

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Yury Smirnov: There are no there were no indications that we cannot do that, even when these detectors were tested at the test beams with the with the different particles, but even if we cannot measure that the TRT and mdt will not saturate like pixel does so.

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Leonardo Rossi: TRT ideally be treated a certain way down to circulate and not not to the readout yeah exactly because it is only 24 once and which you can measure your your.

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Yury Smirnov: know I mean it will be all shifted to the right, but not to the left and since we cut at, for example, here we got it too, we will not be.

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Yury Smirnov: Very affected by that everything will be to the right of the cafeteria to anyway, unlike, unlike the pixel the dx where we can get to the left and that's that's exactly why we can use to add X for both charged categories, but we can you speak so the song, for the first one.

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Leonardo Rossi: You mean that if you have larger than.

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Leonardo Rossi: unionization.

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Leonardo Rossi: Of 10 minutes or 20 Minutes that you will not reach 20 minutes, but he will reach well above two.

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Yury Smirnov: Yes, exactly yes okay.

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Karri Folan DiPetrillo: Okay, so maybe we can move to the more general discussion about these two analyses.

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Karri Folan DiPetrillo: And I think we should try and structure it a little bit, so I think breaking it up into a couple of distinct topics will be helpful.

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Karri Folan DiPetrillo: So first coming back to the pixel dx analysis and any questions about their background estimation or things like that then coming back and following up any questions on the Multi chart analysis and then coming to an overlap or or comparison comparison between the two.

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Karri Folan DiPetrillo: And then maybe finally any sort of future looking questions that we might have so so thank you again to N and Yuri for the really excellent presentations.

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Karri Folan DiPetrillo: If there are any hands or questions about the pixel dx analysis, specifically with respect to the background estimation that'd be really great to get those questions so Danielle.

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Daniele Teresi: hi can you hear me.

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Daniele Teresi: Yes, so yeah this is for an is not that it for the background in estimation, is just a technical question because hi a you mentioned basically when you did all your checks that some of your events like three of them had to mute on.

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Daniele Teresi: associated with the track What do you mean exactly you mean like to me on the same side as the truck or nuance counter balancing the truck or.

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Ann Miao Wang: um so I just mean that there are two two milan's, one of which was the signal track.

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Ann Miao Wang: Okay, so okay okay.

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Okay, thanks.

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Karri Folan DiPetrillo: Okay, maybe, while we wait for.

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Karri Folan DiPetrillo: Some other questions I had one which was.

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Karri Folan DiPetrillo: In the paper you have this this part of uncertainties as a function of the mass and there's some.

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Karri Folan DiPetrillo: Uncertainty that accounts for correlation between the dx and the PT of a truck as a function of mass and it jumps up at about one TV.

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Karri Folan DiPetrillo: And my understanding is this was measured in a validation region with low met and i'm curious if if that uncertainty were to become larger extrapolating to larger met.

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Karri Folan DiPetrillo: With how how much of that excess would it account for, and how can you what sort of studies were done to understand this a little bit better.

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Ann Miao Wang: yeah thanks carrie for that question it's an interesting question so.

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Ann Miao Wang: Right, so this this uncertainty that carries talking about is our largest systematic uncertainty.

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Ann Miao Wang: it's designed to assess any non negligible correlations when we're driving our dx and our momentum toy tracks and right we we do this on our low met control region So the first thing i'll say is that.

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Ann Miao Wang: we're really limited by statistics by this for the systematic and there's some evidence that really the it's.

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Ann Miao Wang: The large size at, especially in our inclusive high regions is really driven by statistics but we're very conservative, so we kind of just go with it and apply it the most conservative uncertainty possible one other thing i'll say about this uncertainty is that really the discrepancy.

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Ann Miao Wang: Any discrepancy that we see with these non negligible correlations are really do to track candidates that are not new on candidates.

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Ann Miao Wang: So we don't expect that this will really affect our new on selection, but again, we do it inclusively because we want to be as conservative as possible and, if I recall correctly, we actually did do.

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Ann Miao Wang: A quick chat but we were very lonely about statistics on whether or not, but if we restrict our mat region if it changed so like a subset of the metro region.

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Ann Miao Wang: Instead of the entire region and we couldn't of course do this in a single regional we could do this in the control region and there wasn't evidence that there was a strong pendants.

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Karri Folan DiPetrillo: There wasn't.

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Ann Miao Wang: wasn't yeah.

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interesting.

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Ann Miao Wang: But.

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Ann Miao Wang: Again we're statistically limited.

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Ann Miao Wang: For this uncertainty so that's the caveat.

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Karri Folan DiPetrillo: Chris do you want go ahead.

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Christopher Hill: yeah sure, my question is about the slide 14 of the previous speakers talk, but I suppose either speaker might want to answer the question so i'll give someone a second to throw that slide up.

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Christopher Hill: Okay yeah thanks very so i'm just want to make sure I understand these column headings and then some of it seems to be.

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Christopher Hill: Leading me to a possibly strange conclusion so I want to check, so, if I understand correctly.

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Christopher Hill: This when you talk about significance de de X significance gets the significance you define a few slides back mom where you divide by this.

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Christopher Hill: Resolution that.

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Yury Smirnov: That Todd was asking about right exactly yes.

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Christopher Hill: Okay, and then, when it says pixel that means just measured in the pixel system and TRT just measured in the TRT system.

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Christopher Hill: Exactly okay so, then you these the values are so different this This to me suggests a problem, do you have.

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Christopher Hill: You have any idea how you can explain a you know 15 Sigma high the the X in the pixels.

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Christopher Hill: But a on the same track, which is past this the quality cuts that we were talking about in the last talk passes the the.

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Christopher Hill: You know, has zero point 16 the TRT significance or or or or the 17 Sigma in the first one actually has a low dx deposition in the TRT I mean what would the likelihood of a track to have that kind of distribution I would think it would be extremely unlikely.

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Yury Smirnov: Yes, this is, this is very unlikely, so let me find the big zodiacs plot.

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Yury Smirnov: So here's the big zodiacs plot, we had 15 there.

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Yury Smirnov: are so this is.

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Yury Smirnov: This is like here.

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Christopher Hill: uh huh.

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Yury Smirnov: And for the TRT we had 4.1 right.

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Christopher Hill: Some of them are even negative yeah.

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Yury Smirnov: yeah or point yeah so oh point one is is like like the peak of the distribution here.

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Christopher Hill: uh huh.

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Yury Smirnov: And negative from just means they are mirrored.

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Yury Smirnov: yeah that's this that's basically it.

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Christopher Hill: or negative doesn't mean it's lower than.

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Yury Smirnov: A negative mean yeah negative means it is a bit lower than.

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Christopher Hill: bit lower.

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Yury Smirnov: than the most bravo.

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Christopher Hill: Bravo, if you take the probability for each point if you go back to the slide 14 and you do this kind of mental calculation of the probability that you just showed for each one.

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Christopher Hill: You can do that, and then multiply it by all seven because they all have this property of having a Heidi the X and the pixels and a low.

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Christopher Hill: probability and the TRT and I think you're getting extremely unlikely number, and I would be interesting to know what that number is.

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Yury Smirnov: OK OK, but these, for example, these two they're not lower he it just It means that you cannot really compare compare exact.

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Yury Smirnov: Well, you alias.

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Christopher Hill: You could do just what you just did I mean they would be closer, so the value would be this one's more probable configuration but.

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Christopher Hill: Yes, but you know you could get the combined probability for all seven seven hits and.

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Christopher Hill: To me this looks like you know, whatever this is is more likely to happen in the pixel detector and I don't understand that because I asked about whether there was a pattern in the radio, distribution and the speaker said it was there wasn't but there looks like there is from from this.

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Leonardo Rossi: No, you say.

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Christopher Hill: Well, you have no basically hi Dr the X in.

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Leonardo Rossi: This they say understand, but they don't understand that the reasoning, why you go from this to the audience dependence.

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Christopher Hill: What the rts further up right.

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Leonardo Rossi: yeah but there's a there's a resolution which is 1000 times lesser than the peaks and then, once a grand radical, which is thousand times less than the pixel.

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Leonardo Rossi: In the database spatial resolution.

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Christopher Hill: So resolution.

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Christopher Hill: yeah I.

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Leonardo Rossi: think you know, speaking of resolutions be here.

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Christopher Hill: yeah yeah but that's not, this is an energy measurement so.

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Leonardo Rossi: Yes, but you you claim is a problem of mix.

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Leonardo Rossi: mix up of tracks and.

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Christopher Hill: that's not what I say that's one possibility but yeah I agree with a pixel detector that's quite unlike with.

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Leonardo Rossi: Out of this matrix what I find the most striking is the fact that there are two events which are in the far pain of the distribution the number three and number four, which are the fairy tale of both distribution, where there is a measurement.

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Leonardo Rossi: So there is that, yes, I agree that it is very unlikely you, you may have a fluctuation in the in the pixel and not every fluctuation in tfp so.

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Christopher Hill: Well, but maybe it also goes to the thing that Todd said, maybe the Sigma I mean you're talking to these if we're to read this correctly, you have flux, then those are not fluctuations 17 Sigma is not a fluctuation that happens.

375
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Leonardo Rossi: What what is 17 Sigma.

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Christopher Hill: Well yeah.

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Stefano Passaggio: It is seven.

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Stefano Passaggio: Dec bow the core Sigma.

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Christopher Hill: yeah so.

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Stefano Passaggio: What we're haviland lt.

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Christopher Hill: Oh OK.

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Christopher Hill: I see so that's if you assumed, it was gaussian it's yeah yeah so again that's.

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Christopher Hill: Maybe not the point that that doesn't reflect the actual probability it's not really out of Sigma like.

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Todd Adams (he/him/his): that's my conclusion, Chris.

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Christopher Hill: Yes, okay.

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Karri Folan DiPetrillo: yeah I also I sort of along this line I would be very curious to understand exactly what the the X values the TRT and mdt measurements point to.

387
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Karri Folan DiPetrillo: And also seeing sort of like what the underlying.

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Karri Folan DiPetrillo: At ABC or time over thresholds like what what those underlying distributions look like for signal and background.

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Karri Folan DiPetrillo: Because I think Todd was asking a question about can Can someone was asking a question about Ken the TRT and the mdt actually measure GD X then close on this time.

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Karri Folan DiPetrillo: It would be very it would be convincing to sort of seen those those like low level distributions Okay, and the next question or the next hand that I see from Thomas.

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01:00:11.400 --> 01:00:24.180
Tamas Almos Vami: hi I was wondering how does the first deal with the merge clusters, I guess it for both of these folks so, then you have tags from two variables to each other and instead of having two clusters it's much faster.

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Yury Smirnov: So if you may have the.

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Yury Smirnov: pixel kids which are shared between at least two tracks right.

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01:00:33.180 --> 01:00:35.310
Yury Smirnov: Is that is another question.

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Tamas Almos Vami: yeah I guess yes.

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01:00:38.850 --> 01:00:41.010
Yury Smirnov: Yes, so we.

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01:00:42.270 --> 01:00:54.000
Yury Smirnov: We rejected such strikes work where that the right exactly zero such hits on an ID track which first wants to the to the current the human.

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01:00:55.800 --> 01:01:01.380
Tamas Almos Vami: Rights so that's kind of my patient, how do you know that that cluster is actually coming from two tracks in through one.

399
01:01:07.380 --> 01:01:16.860
Yury Smirnov: Or, yes, this is decided by the neural network that does the big sort of pixel tracker construct the I mean the the inner inner the director trickier construction.

400
01:01:19.620 --> 01:01:23.760
Yury Smirnov: Somebody with the big solar expertise will know better, yes.

401
01:01:24.750 --> 01:01:29.130
Tamas Almos Vami: And these the same neural network, using the previous talk Tuesday.

402
01:01:31.380 --> 01:01:31.830
Tamas Almos Vami: Yes.

403
01:01:32.100 --> 01:01:32.700
Yes.

404
01:01:41.790 --> 01:01:42.150
Karri Folan DiPetrillo: Okay.

405
01:01:43.230 --> 01:01:44.610
Karri Folan DiPetrillo: Are you comfortable with that answer.

406
01:01:45.720 --> 01:01:50.850
Tamas Almos Vami: Well, I would like to hear more about it, if you have some pointers to do is not to be nice.

407
01:01:52.080 --> 01:01:57.120
Tamas Almos Vami: Do the answers in the machine learning is that it somehow happens so yeah.

408
01:01:57.810 --> 01:02:09.450
Karri Folan DiPetrillo: I will in there is a paper on the neural networks for pixel clustering and others I don't know how old it is perfect I believe it's out there.

409
01:02:12.030 --> 01:02:14.070
Karri Folan DiPetrillo: Okay, then moving to Eric.

410
01:02:15.420 --> 01:02:17.970
Eric Chabert: Thanks a lot so it's a question for the first speaker.

411
01:02:19.830 --> 01:02:25.440
Eric Chabert: So mean about the seven track that you're isolated you, you said several several things about the.

412
01:02:26.400 --> 01:02:37.830
Eric Chabert: How much of them were matching the system number of events where you had to dance, but he didn't say anything about the the main distribution, did you check what the angles, like the delta Phi between the.

413
01:02:38.460 --> 01:02:49.920
Eric Chabert: cultivate and the machine at all these kind of things, and the second question is about the events with unions, did you check in there were close to each other if the the.

414
01:02:51.030 --> 01:02:54.240
Eric Chabert: The two meals were comfortable with resonance like a gypsy or something.

415
01:03:01.980 --> 01:03:04.980
Ann Miao Wang: um yeah so give me one second.

416
01:03:09.570 --> 01:03:14.010
Ann Miao Wang: i'm looking to see if I put them and distribution and the slides and I didn't.

417
01:03:16.800 --> 01:03:22.170
Ann Miao Wang: So I guess my question for you is what do you what's the check for them and distribution.

418
01:03:23.970 --> 01:03:31.920
Eric Chabert: I mean you in some events, you said that if it has made this table, we expect the particles to count for the balance so I just would like to know if it's.

419
01:03:32.250 --> 01:03:41.940
Eric Chabert: aligned with the track of align with the jets or in there is no correlation at all evil, which was to make contact me the candidates, all the other ditch in back to back.

420
01:03:43.020 --> 01:03:44.490
Eric Chabert: that's that's the question.

421
01:03:45.540 --> 01:03:58.140
Ann Miao Wang: Right so for some advanced the mentors along the star trek and for something that meant yeah the mat is opposite, so this isn't really a consistent thing along across all of the signal region.

422
01:03:59.340 --> 01:04:00.630
Ann Miao Wang: Access candidates.

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01:04:03.420 --> 01:04:03.690
Eric Chabert: Okay.

424
01:04:04.230 --> 01:04:05.640
Ann Miao Wang: Sorry what's your second question.

425
01:04:05.850 --> 01:04:18.180
Eric Chabert: And the second question was doing the angle between the champions that we have for events was given to the answer but mean did you check the the angles between the ones where they are close to each other, wrong.

426
01:04:19.290 --> 01:04:20.280
Eric Chabert: or not necessarily.

427
01:04:22.500 --> 01:04:26.340
Ann Miao Wang: I don't remember this information Maybe someone else or whoever's.

428
01:04:31.380 --> 01:04:37.020
Leonardo Rossi: There are relatively Why then go to new ones, but I don't remember exactly the.

429
01:04:38.580 --> 01:04:44.370
Leonardo Rossi: And these are momentum me on, so I would expect, and not to be shape side.

430
01:04:45.690 --> 01:04:47.070
Leonardo Rossi: But they cannot confirm.

431
01:04:52.740 --> 01:04:53.100
Ann Miao Wang: Okay.

432
01:04:54.600 --> 01:04:54.930
that's.

433
01:05:00.630 --> 01:05:02.190
Matt Strassler: Okay i'm trying to put.

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01:05:02.190 --> 01:05:03.060
together.

435
01:05:04.200 --> 01:05:15.450
Matt Strassler: The slide 37 from top which shows the PT track distributions and and and uris list of events and try to understand what's going on with the pts of these.

436
01:05:16.380 --> 01:05:29.070
Matt Strassler: tracks, if I understand correctly, there if we look at the PT distribution on slide 37 a man's talk the seven events that we're talking about are separated from the rest of the distribution Is that correct.

437
01:05:32.490 --> 01:05:38.220
Matt Strassler: And then the question which would follow that whether the answer is yes or no, I suppose, is what do we make of the.

438
01:05:38.700 --> 01:05:44.190
Matt Strassler: In the five events where there's immune system measurement of the of the track PT the difference between the.

439
01:05:44.550 --> 01:05:55.260
Matt Strassler: track PT and the and the, and the immune system PT tracker PT and immune system pts is large, and as a theorist I don't have a sense for whether it's spectacular large or just a little unusually large or what.

440
01:05:59.640 --> 01:06:00.600
Ann Miao Wang: i'm so.

441
01:06:01.770 --> 01:06:02.220
Ann Miao Wang: Sorry.

442
01:06:02.280 --> 01:06:04.230
Leonardo Rossi: Did someone say something where we're headed and.

443
01:06:04.830 --> 01:06:12.510
Ann Miao Wang: So I know that for for the new on candidates, we did track them beyond momentum and it's consistent with the track beauty.

444
01:06:15.150 --> 01:06:17.400
Ann Miao Wang: And then, let me share this slide.

445
01:06:22.470 --> 01:06:22.680
Ann Miao Wang: well.

446
01:06:26.430 --> 01:06:36.060
Ann Miao Wang: here and I don't recall if all of the access events exactly match up with this Maybe someone else does if that's what you're asking them.

447
01:06:36.240 --> 01:06:43.500
Matt Strassler: What we're looking at your if I don't know if you can put up this slide again let's just look just know that there's a gap around 500 gv and.

448
01:06:43.950 --> 01:06:46.590
Matt Strassler: very competitive his slide of the list of events.

449
01:06:50.250 --> 01:06:53.340
Yury Smirnov: yeah you're talking about this difference rate.

450
01:06:53.610 --> 01:06:55.710
Matt Strassler: Right so they're all about 500 gv.

451
01:06:55.950 --> 01:06:57.330
Matt Strassler: First box have number four.

452
01:06:58.560 --> 01:07:07.560
Matt Strassler: And for all the ones where there's a new on measurement immune system measurement and Atlanta inner detector measurement there's a substantial discrepancy, although again I.

453
01:07:09.180 --> 01:07:10.920
Matt Strassler: In those measurements they're they're quite different.

454
01:07:12.930 --> 01:07:17.910
Matt Strassler: And I just wonder, you know, do you see this in typical nuance or is this way on on Intel.

455
01:07:18.090 --> 01:07:23.670
Yury Smirnov: No, we do not see this, I think, in a typical here for the typical humans.

456
01:07:24.990 --> 01:07:40.980
Yury Smirnov: This is yes, this request the detailed study that may mean that these particles lose some non negligible non human like energy num num you like amount of energy and kill remembers.

457
01:07:41.550 --> 01:07:43.320
Matt Strassler: So that should be observable in the in the.

458
01:07:44.250 --> 01:07:47.910
Karri Folan DiPetrillo: Yes, is that a true statement that this effect wouldn't be consistent with.

459
01:07:48.570 --> 01:07:57.060
Karri Folan DiPetrillo: The PT resolution for very high PT intro detector tracks like I would imagine, because you only have a few so we can hit repeat it could be skewed.

460
01:07:57.690 --> 01:08:10.350
Karri Folan DiPetrillo: upwards, and then even for TV you on your pizza resolution is on the order of 10% in the in the barrel so for the inner detector, it must be much larger uncertainty.

461
01:08:13.020 --> 01:08:13.290
Yury Smirnov: Right.

462
01:08:13.320 --> 01:08:18.990
Matt Strassler: which then in turn folds into the mass uncertainty and and so forth, that one extract.

463
01:08:21.600 --> 01:08:23.070
Leonardo Rossi: You should be able to see.

464
01:08:23.100 --> 01:08:31.860
Michael Albrow: yeah it's gonna come in, if you go back to the plot before showing the PG distribution and the tracks when you share before.

465
01:08:32.100 --> 01:08:34.650
Karri Folan DiPetrillo: I think this is an support that john.

466
01:08:35.070 --> 01:08:35.340
Oh.

467
01:08:40.950 --> 01:08:41.250
Matt Strassler: Right.

468
01:08:41.910 --> 01:08:42.360
So.

469
01:08:43.770 --> 01:08:45.060
Michael Albrow: Pretty, for example, the.

470
01:08:46.350 --> 01:08:57.690
Michael Albrow: The highest PT track the uncertainty on the PT is will not be gaussian it'll be very, very large horizontal area band really on that on that particular event right.

471
01:09:00.720 --> 01:09:02.550
Ann Miao Wang: yeah, so I think I actually have.

472
01:09:03.540 --> 01:09:04.860
Some simulation.

473
01:09:07.650 --> 01:09:19.050
Ann Miao Wang: Here right so it's not super dashing so here's the relative one over PT resolution so where this is quantified as the full with half maximum do it by the factor to convert it sort of to Sigma.

474
01:09:20.340 --> 01:09:31.560
Ann Miao Wang: And here are maybe ignore that decay, but here, look at the different slices of PT to get a sense of what the resolution will look like and it's it's.

475
01:09:32.820 --> 01:09:36.420
Ann Miao Wang: Almost 1600 percent at this hype momentum.

476
01:09:41.460 --> 01:09:41.850
Michael Albrow: yeah.

477
01:09:43.680 --> 01:09:53.370
Karri Folan DiPetrillo: I think this is yeah This is consistent with what I would expect Okay, so I see two more hands, and I think after that we should move to the next presentation so Chris do you have one more question.

478
01:09:54.360 --> 01:09:55.050
Christopher Hill: This is a quick.

479
01:09:55.200 --> 01:09:58.920
Christopher Hill: clarification for the for the multiple charged particle analysis.

480
01:10:00.210 --> 01:10:08.220
Christopher Hill: Of course it if they were really multiply charged the p the PT measurement that you would make normally make is wrong by that factor right.

481
01:10:09.510 --> 01:10:13.290
Christopher Hill: But all the measurements to show, and these tables and whatnot are just assuming the charges one.

482
01:10:14.310 --> 01:10:18.750
Yury Smirnov: Is there for years, because this is in data we always assume the Church.

483
01:10:19.560 --> 01:10:19.980
Yury Smirnov: that's what I.

484
01:10:21.210 --> 01:10:21.930
Christopher Hill: OK OK.

485
01:10:25.020 --> 01:10:26.550
Karri Folan DiPetrillo: OK, and then one last thing.

486
01:10:28.110 --> 01:10:31.350
Malgorzata Kazana: I would like to ask, and about how well they.

487
01:10:31.500 --> 01:10:32.790
Malgorzata Kazana: Quote make the.

488
01:10:33.480 --> 01:10:47.250
Malgorzata Kazana: calibration of the dx how what what is the final result of the plot on the page number five, which is the dx versus luminosity after all corrections.

489
01:10:52.890 --> 01:11:02.610
Ann Miao Wang: it's it's flat, so we we we measure it for each run so each atlas run and we correct it to one point.

490
01:11:04.830 --> 01:11:05.070
Ann Miao Wang: For.

491
01:11:05.730 --> 01:11:06.900
Ann Miao Wang: A flat for maps.

492
01:11:07.620 --> 01:11:17.730
Malgorzata Kazana: Okay, and then yeah the the other question is how, in time, the candidates events are distributed.

493
01:11:18.870 --> 01:11:23.850
Malgorzata Kazana: We have the numbers which is shown in the other presentation, but I have no idea.

494
01:11:24.960 --> 01:11:29.610
Malgorzata Kazana: What is the time difference between different parts of the data taking.

495
01:11:31.200 --> 01:11:36.300
Malgorzata Kazana: So they are from 2017 or 18 or 16.

496
01:11:42.960 --> 01:11:50.610
Ann Miao Wang: or ask the access events what they were destroyed how they're distributed across run to.

497
01:11:52.320 --> 01:11:53.250
Ann Miao Wang: Is that what you're asking.

498
01:11:53.910 --> 01:11:56.190
Malgorzata Kazana: Yes, yes, if they are in.

499
01:11:57.420 --> 01:12:11.520
Malgorzata Kazana: Because in the other presentation we have the random number between three zero for up to 364 thousand and I wonder if it is coming from a certain period of time or it is.

500
01:12:13.830 --> 01:12:19.980
Malgorzata Kazana: You have taught events in 2017 five insert 18 or something like that.

501
01:12:21.150 --> 01:12:26.790
Ann Miao Wang: yeah there well distributed across the run to data set but I don't have the numbers which are.

502
01:12:26.820 --> 01:12:30.000
Leonardo Rossi: unique because they are not clustered in any place.

503
01:12:31.980 --> 01:12:32.640
Malgorzata Kazana: Thanks.

504
01:12:35.190 --> 01:12:36.930
Karri Folan DiPetrillo: Okay Thank you everyone for.

505
01:12:36.930 --> 01:12:42.300
Karri Folan DiPetrillo: The lively discussion, I think we should move to the next talk.

506
01:12:43.470 --> 01:12:44.820
Karri Folan DiPetrillo: From Daniel a on.

507
01:12:46.020 --> 01:12:51.420
Karri Folan DiPetrillo: Heidi excellence from boosted multi charge long of particles.

508
01:12:52.290 --> 01:12:55.140
Daniele Teresi: lot, thank you very much, can you see my screen.

509
01:12:55.740 --> 01:12:56.580
Yes, we can.

510
01:12:57.600 --> 01:12:58.530
Karri Folan DiPetrillo: hear and see your.

511
01:12:59.190 --> 01:13:10.410
Daniele Teresi: Perfect thanks a lot good afternoon everybody thanks for the invitation and we will talk about this reason work of mine with religion Jewish and much matala here are certainty ah.

512
01:13:10.860 --> 01:13:18.900
Daniele Teresi: And we're going to build up on the experiment results that we just seen any particular on the excel the dx access, assuming that it's real, of course.

513
01:13:19.530 --> 01:13:28.650
Daniele Teresi: As we just heard you know our policies seven events in a region which is basically signal dominated in which the background is supposed to be very small.

514
01:13:29.550 --> 01:13:37.230
Daniele Teresi: And the reason we the height, etc, so I would that be to have the other staff and the reason why the ground here is so small it's very simple to understand.

515
01:13:37.860 --> 01:13:53.790
Daniele Teresi: Also on the particles that we have have large dx if they are relatively slow, but at the same time having details of federal crumbled cats them cuts, all of them away so that either fast or in or have a large the dx.

516
01:13:55.260 --> 01:14:04.410
Daniele Teresi: In terms of bsm we heard that artist analyzes them terms of particles we beat of the order of 0.5 0.6 and these excess of.

517
01:14:05.010 --> 01:14:14.820
Daniele Teresi: about six events survives all checks about from one which is that all these access seems to have a large data from that act time of flight measurements.

518
01:14:15.480 --> 01:14:29.880
Daniele Teresi: And this is precisely what caught our attention, what will our starting point, our main motivation to see if indeed it's possible to reconcile this access with the information on the time of flight that tells us essentially details of the order one is very close to one.

519
01:14:31.080 --> 01:14:42.330
Daniele Teresi: And we believe it's possible, and the reason is, besides the best of block curve, the best of luck curve, which gives the most probable value of the dx the pens on a bunch of quantity, some of them are.

520
01:14:43.290 --> 01:14:50.700
Daniele Teresi: Material dependent so dependent on the detector just to have them depends on the particle is promising your detector.

521
01:14:51.030 --> 01:14:58.650
Daniele Teresi: One is of course the speeds that philosophy B or B to gamma and the other one is the charge of the box, because we heard in the Second, the second talk.

522
01:14:59.100 --> 01:15:06.780
Daniele Teresi: And, in particular, the average energy deposition he had every germination depends on the square of the charge of the particle.

523
01:15:07.230 --> 01:15:17.670
Daniele Teresi: Then, if you plot if you just do the simple exercise to plot the dx as function of beta for the Q equal to one charge, but one and charge equal to two hypothesis.

524
01:15:18.240 --> 01:15:28.470
Daniele Teresi: You see, that it is true that you can interpret the access in terms of beat, of the other of 0.5 is great band is precisely the region where others season access.

525
01:15:28.980 --> 01:15:39.720
Daniele Teresi: it's also true that you can interpret it says as Q equal to two as long as beta is besides, of the other one, and this on the other hand, is perfectly what.

526
01:15:40.050 --> 01:15:50.430
Daniele Teresi: The time of flight measurements seems to suggest So if you want to look into to fit both in both data sets of data, which is the larger organization and the.

527
01:15:50.730 --> 01:15:54.870
Daniele Teresi: Time of flight measurement you're immediately led to consider the hypothesis.

528
01:15:55.470 --> 01:16:04.710
Daniele Teresi: Which these events have originates from to equal to two but boosted and so not slow articles in the previous analysis, what we heard an abuse analysis.

529
01:16:05.010 --> 01:16:11.880
Daniele Teresi: covert the case in which is tragic or two to was of the order of 0.5 0.6 or whatever and.

530
01:16:12.360 --> 01:16:25.200
Daniele Teresi: We saw that is not significant access their what you're trying to propose here is a different position which we, we have to equal to two but boosted, and this is basically what is suggested by the combination of these two sets of measurements.

531
01:16:26.430 --> 01:16:36.090
Daniele Teresi: Now the problem here is that the others analysis is performed in terms of equal to one and we want to interpret it as Co equal to do that i'm talking about the pixel analysis.

532
01:16:37.410 --> 01:16:46.980
Daniele Teresi: Let me recap very short from a serious point of view across protocol to measure these build disease surveillance or we saw that he has the following.

533
01:16:47.460 --> 01:17:00.210
Daniele Teresi: Those measures, the momentum and the D dx and then, as we heard that you can invert the better block curve to find beta gamma of this tracker and once we have that you can.

534
01:17:00.690 --> 01:17:09.870
Daniele Teresi: From beta gamma and measured momentum up, you can obtain an estimate for the mass which i'm going to call it here like an effective mass mdx.

535
01:17:10.470 --> 01:17:16.140
Daniele Teresi: Which is for equal to one an approximation to the physical mass of your particle.

536
01:17:16.650 --> 01:17:25.230
Daniele Teresi: and saying that it's an approximation, because we heard those requests and so these if you look at the histograms, this is far from being monochromatic so.

537
01:17:25.950 --> 01:17:32.310
Daniele Teresi: Even if you assume that the signal here is monochromatic what you see in your detect what you see in the histogram with the record start sorry.

538
01:17:32.670 --> 01:17:40.560
Daniele Teresi: it's a large very large distribution, especially if you can see a masters of your little tab which are the ones of interest for to explain the access.

539
01:17:41.130 --> 01:17:50.400
Daniele Teresi: And this is you basically to do affects that first and most important effect is that the grading momentum resolution for a minute on ios test.

540
01:17:50.850 --> 01:17:55.590
Daniele Teresi: The resolution of the tracker is basically of the other 100% almost.

541
01:17:56.580 --> 01:18:05.820
Daniele Teresi: The the second effect is the natural spread of the the the X distribution around the most probable body which is even by the lung now distributions that was discussed before.

542
01:18:06.540 --> 01:18:10.410
Daniele Teresi: Once you take into account these two effects were able to reproduce basically the.

543
01:18:11.070 --> 01:18:18.810
Daniele Teresi: The histograms given by the collaborations, and so we were confident that we could simulate physics, the physics, that we wanted that.

544
01:18:19.710 --> 01:18:28.530
Daniele Teresi: So i'm going to call these just mdx on effective mass and not the physical mass, because this is going to be important for what i'm going to say immediately after.

545
01:18:29.010 --> 01:18:36.180
Daniele Teresi: And, in particular, since we want to interpret to equal one the two equal to one search in terms of three equal to two fees, except.

546
01:18:36.540 --> 01:18:45.240
Daniele Teresi: We have to do a trick, we cannot interpret directly the data just because, for instance, we don't have the information for the detailed information of the time of slide.

547
01:18:45.690 --> 01:18:53.610
Daniele Teresi: What we can do is just to use a tree, and in particular what we can do is to assume to equal to to be understand the policies, except.

548
01:18:54.240 --> 01:19:05.370
Daniele Teresi: and run it through that Q equal to one atlas protocol that was described before and see how the skew equal to one Protocol would see these two equal to two physics.

549
01:19:06.180 --> 01:19:17.850
Daniele Teresi: In particular, why we can simulate this critical to do physics, because we can approximate that the average organization of chemical to do physics, is just four times the ones that has been calibrated and discussed before.

550
01:19:18.570 --> 01:19:22.020
Daniele Teresi: But now seems kit now there is a mismatch between the charge.

551
01:19:22.470 --> 01:19:28.980
Daniele Teresi: Of the vm charge of your part of the physical particles and the charge assumed in the construction protocol.

552
01:19:29.280 --> 01:19:38.280
Daniele Teresi: Clearly, these effective mass is the output of this Protocol would not be an indicator of the physical mass of the particles would be somewhere else notice also that.

553
01:19:38.700 --> 01:19:48.900
Daniele Teresi: Something similar is true for the momentum that medical sector measurement is assumed to equal to do is not the true momentum your part, to go, but there is a mismatch of actors.

554
01:19:49.350 --> 01:20:02.190
Daniele Teresi: And a few of these factors are conspiring in such a way, basically, that the effective mass is the output of the protocol is not the physical is quite different from the physical mass of the article.

555
01:20:03.360 --> 01:20:15.480
Daniele Teresi: But this doesn't matter, because this is a perfect perfectly legitimate protocol that we can follow to just to build histograms to build signal that's a news the signal models to fit the access, according to the seasons, that we are proposing.

556
01:20:16.020 --> 01:20:24.840
Daniele Teresi: and remind you that the physics and we're proposing is equal to to both said, and to have it, what we assume is that there is apparent razon saying.

557
01:20:25.410 --> 01:20:34.200
Daniele Teresi: Which is produced, for instance by Israel yeah not or bloomfield genre and this parent restaurant, which is heavy decays into two equal to two.

558
01:20:34.620 --> 01:20:43.950
Daniele Teresi: Though the path he goes, and this daughter particles will naturally be boosted if this P is heavy enough as much heavier than than the daughters, in particular.

559
01:20:45.090 --> 01:20:53.190
Daniele Teresi: So we can run these and we can build signal models, we can build histograms and we can feed the data which are these black points here.

560
01:20:54.960 --> 01:21:03.270
Daniele Teresi: By using the signal models, in particular, this is just a benchmark model in which the parent resonance as a five TV and daughters are a time with gv.

561
01:21:03.840 --> 01:21:15.600
Daniele Teresi: You see that in read this is the background is taken from the Atlas analysis, whereas Green is the background, plus our signal mandala after you close use the Q equal to one.

562
01:21:16.290 --> 01:21:19.590
Daniele Teresi: Reconstruction algorithm and you see that access can be perfect.

563
01:21:19.920 --> 01:21:34.320
Daniele Teresi: Perfectly Basically, this is the mdx histograms but as a check and not just as a check, we also looked at the pts programs and add access to bounce the pts tokens were discussed before there was some discussions about this gap, but.

564
01:21:34.710 --> 01:21:38.520
Daniele Teresi: Well, this gap is perfectly compatible, you know with the background and.

565
01:21:39.810 --> 01:21:49.950
Daniele Teresi: The signal mandala and again, you see that here the basically all the signal is a large bts at BT larger than 700 gem of soul.

566
01:21:50.280 --> 01:21:58.470
Daniele Teresi: And the reason why you can isolate it from that background is because I remind you that there is a large dx can't assume in this kind of histograms.

567
01:21:59.160 --> 01:22:09.210
Daniele Teresi: For the dx the we can one cannot see the one cannot distinguish the access from the background, but at least our signal model does not ruin what.

568
01:22:09.780 --> 01:22:17.550
Daniele Teresi: The observation basically So all in all, you see, that you can fit all available histograms with this simple physical hypothesis so.

569
01:22:18.330 --> 01:22:27.030
Daniele Teresi: Then, that means that you can perform about our feet and particularly to just stand up some other things here we do a profile likely feet with bostonian likelihoods.

570
01:22:28.140 --> 01:22:34.710
Daniele Teresi: less likely will in our analysis slightly different format us what we do is we feed all these three histograms.

571
01:22:35.010 --> 01:22:48.090
Daniele Teresi: Just because we want to use all available information, especially that information, otherwise the results will be slightly misleading, but to do so, we have to build the toys with experiments estimate confidence intervals and so on and so forth.

572
01:22:49.260 --> 01:23:03.930
Daniele Teresi: And most importantly in this plot that i'm going to show what is taken into account, is just the boost in production so is proton broaden that creates that uses our residents P, which decays into those particles of charge equal to.

573
01:23:05.280 --> 01:23:20.790
Daniele Teresi: Assuming that just this production now what you see that another space of the model, the current status of the scenario so which is basically daughters up to 1.5 TV let's say and parent resonances from sleep on TV or hey.

574
01:23:21.990 --> 01:23:34.260
Daniele Teresi: You can feed the access in all these regions, most importantly, by the very construction the beta that you have here is large as close to one because these guys are boost if you have a five.

575
01:23:35.670 --> 01:23:37.980
Daniele Teresi: Days once the gains into one.

576
01:23:39.360 --> 01:23:48.840
Daniele Teresi: part because these will have a beat, of the other 0.9 or lab so these by constructions we fit also the time of flight information.

577
01:23:51.060 --> 01:23:55.410
Daniele Teresi: predictions and be shot the direction of the scenario where there are at least two.

578
01:23:55.830 --> 01:24:06.720
Daniele Teresi: irreducible or almost reusable additional prediction The first one is the fact that okay these guys have charged equal to do so they can be produced directly electromagnetically.

579
01:24:07.230 --> 01:24:17.730
Daniele Teresi: And this is precisely the search that we saw the second talk, we saw my data set, and this is precisely the production mechanism this, considering that analysis and.

580
01:24:18.150 --> 01:24:28.260
Daniele Teresi: As we heard that these will give you an even larger the yet so the reusable for the event using both prediction here is that, in association to these events, you should have also.

581
01:24:28.680 --> 01:24:46.500
Daniele Teresi: event, with a much larger the dx maybe closer to the dynamical range of the Torah, and in particular the limits of we heard about from last week kept a good portion of this parameter space essentially they got all these before 1000.

582
01:24:47.730 --> 01:24:52.650
Daniele Teresi: There is a slight 1.5 Sigma access and that's clearly is not significant.

583
01:24:53.970 --> 01:25:05.040
Daniele Teresi: piece at this moment, but we should keep an eye on that, of course, and by casting this good chunk of the parent the space, we see that the parent restaurant has to be pretty heavy let's say five TV or heavier.

584
01:25:05.730 --> 01:25:10.560
Daniele Teresi: And the second almost reusable prediction the scenario is that since that.

585
01:25:11.070 --> 01:25:19.500
Daniele Teresi: parent resonance is produced by protein protein Now you can just flip the diagram and see that this parent restaurant should be able to decay into digests.

586
01:25:19.920 --> 01:25:28.380
Daniele Teresi: And so they should give you should be seeing also a digest see in a lot with the corresponding mass, and this is true, up to.

587
01:25:28.860 --> 01:25:38.550
Daniele Teresi: The magnitude of the signal is basically determined by the exact size up to the relative branching ratio into this know the particles and the jets.

588
01:25:39.120 --> 01:25:49.680
Daniele Teresi: And again here, you can consider the game to like walk so thank you once and you see, these are the relevant bounce and even here, you have to seek Max size of around the sides DVD.

589
01:25:52.380 --> 01:25:53.610
Daniele Teresi: microscopic models.

590
01:25:55.170 --> 01:26:07.920
Daniele Teresi: independently, so we want to a physical model of harding apparent resonance became into to charge to park because and that from the model building point of view, the most important, the most interesting aspect of this stage.

591
01:26:08.730 --> 01:26:19.470
Daniele Teresi: is dependent resin so, in particular, there are a few options for the present presence in the the simplest options are if the parent resonance is neutral and the standard model.

592
01:26:19.800 --> 01:26:30.360
Daniele Teresi: So it could be either a scale of couple new ones or that prime couple towards so if it's a still a couple to blue ones essentially what you're assuming is that the spelling bee.

593
01:26:30.810 --> 01:26:37.830
Daniele Teresi: A couple new ones with some energy scale, along with some interaction scale on that and couples, of course, to this loaded particles.

594
01:26:38.250 --> 01:26:48.510
Daniele Teresi: But if you just look at numbers, if you want to reproduce the best fit for sexual that signal of the for the dx access, you see immediately that this has to be a strongly cobbled.

595
01:26:48.780 --> 01:26:58.890
Daniele Teresi: Reza answer, so the predictions here is that there should be some sort of medical sector not far away in master from the five TV or whatever, a mass scale.

596
01:27:00.090 --> 01:27:14.880
Daniele Teresi: of interest here you have turn a deaf is at that prime which can be able to quote Sir, and if you take into account that this has to be produced by by quarter by light box, it has to be coupled with like to like quotes for sure.

597
01:27:16.140 --> 01:27:25.980
Daniele Teresi: Whereas the carbon black dots will give you a strong balance so in this case does that promise to be more than at any level of all the not match it stall or the one numbers here, but the.

598
01:27:26.340 --> 01:27:37.020
Daniele Teresi: ratio of charges of the of the of the electrons and daughter particles has to be a number, which is in favor of the daughter part because of the of the CEO or the one.

599
01:27:37.740 --> 01:27:52.050
Daniele Teresi: Then the other, the last options is that this parent restaurant is charged either under Su to weaker or color, this is a bit more difficult accommodate outdoor it could be useful to modern build a DK into.

600
01:27:53.370 --> 01:28:09.330
Daniele Teresi: notice, which are not the same, so if this is not the same that clearly cannot became a pair of the same path because, and this could be a good option if future data would show that you always have just one track and not two tracks That gives you a large dx.

601
01:28:10.920 --> 01:28:11.520
Daniele Teresi: To conclude.

602
01:28:12.990 --> 01:28:27.600
Daniele Teresi: Well, with the dx access could be, of course, a non physical background, but this I mean from a serious point of view, seems unlikely, because you know I cannot imagine what some other particle could be, at the same time, slow and give you and have a PT or.

603
01:28:29.880 --> 01:28:37.590
Daniele Teresi: It could be a statistical situations and these again from a from a serious point of view is very unlikely, because this is all these things are four Sigma or more.

604
01:28:38.640 --> 01:28:46.260
Daniele Teresi: It could be an experimental each one of these have of course no idea or it could be new physics, and this is easy because it's just simple.

605
01:28:46.770 --> 01:28:51.900
Daniele Teresi: We just need to wait, we need to wait for cms to see if they see the same things and.

606
01:28:52.320 --> 01:29:06.990
Daniele Teresi: likely enough, we don't need to wait for long because with the data of run three should be enough to bring these access to more than 60 month if you got the best fit cross sections and these is pretty much for the wonderful Italian thanks.

607
01:29:10.440 --> 01:29:18.060
Margaret Lutz: I didn't really think so much first time so right it's really interesting to hear the female perspective and.

608
01:29:19.020 --> 01:29:30.270
Margaret Lutz: Also, I guess you're already managed to incorporate a little bit about the recent mcp results into your talk so that's that's my sake, and then I see We already have a couple questions so Leonardo, do you want to go ahead, yes.

609
01:29:30.840 --> 01:29:40.350
Leonardo Rossi: I mean all this theory is not broken by the table which has been shown by by the teacher would be multi charge.

610
01:29:43.980 --> 01:29:48.330
Leonardo Rossi: analysis on the seven events and those seven events or not.

611
01:29:49.440 --> 01:29:52.410
Leonardo Rossi: I unionization anywhere else, apart from.

612
01:29:54.930 --> 01:29:55.290
Daniele Teresi: well.

613
01:29:57.210 --> 01:30:01.440
Daniele Teresi: Of course, that at least in the pressure of the search for the for the highest charge.

614
01:30:01.770 --> 01:30:10.980
Daniele Teresi: of analysis of the art class they take into account, they can see that a different production mechanism so they're not talking about booster packs are talking about particles produced by.

615
01:30:11.370 --> 01:30:20.790
Daniele Teresi: A recent photo infusions or drill Yun which will be slow and would have a much larger ionization energy than the one that we expect, so this is our first.

616
01:30:21.690 --> 01:30:31.050
Leonardo Rossi: yeah sure, but I mean they have taken the seven events and out for the seven events they ever look to the unionization in theory to your musician mdt.

617
01:30:31.380 --> 01:30:40.500
Leonardo Rossi: And the only there are only two events which by the way, or the norm, you want events where this is in some sense confirm that the other five.

618
01:30:40.980 --> 01:30:41.340
Daniele Teresi: I mean.

619
01:30:41.460 --> 01:30:52.650
Leonardo Rossi: These so so if this article is is a challenge to, then it is not any more challenged to at least four or five out of seven at when we return from the pixel.

620
01:30:53.400 --> 01:31:02.880
Daniele Teresi: So it can be gale force, I mean these guys do not need to be saved right they can became they can indicate soon enough they became a charge one objects or to charge one objects so.

621
01:31:02.940 --> 01:31:03.810
Leonardo Rossi: These yeah but they.

622
01:31:06.210 --> 01:31:11.250
Leonardo Rossi: should continue the same path because these practices are constantly throughout the APP to the new one.

623
01:31:11.550 --> 01:31:20.670
Daniele Teresi: But that was a judge one particle would be just background, not the dx right boost because I mean the charge to object is charged right and.

624
01:31:21.750 --> 01:31:29.910
Daniele Teresi: So he does he was the one if it became feel lucky enough that the case essentially between the pizza I mean the decay length of the order of the pixel that extra.

625
01:31:30.240 --> 01:31:39.630
Daniele Teresi: Clearly, you know the product would be boosted by charge one, and at that point, it would be just the ground, it would be indistinguishable from the ground up, and this is one option now.

626
01:31:40.950 --> 01:31:51.690
Leonardo Rossi: yeah but he does, I mean, since we reconstruct the track to out must continue the same path from being challenged to to becoming charge, one which is very in line.

627
01:31:52.980 --> 01:31:55.470
Daniele Teresi: indicates is not than likely if they decay.

628
01:31:55.470 --> 01:31:56.970
Daniele Teresi: leg, that is the correct.

629
01:31:57.000 --> 01:32:01.410
Leonardo Rossi: yeah before he became he became the same same the same direction, independent.

630
01:32:01.800 --> 01:32:02.640
it's booster.

631
01:32:03.660 --> 01:32:07.170
Daniele Teresi: For us, the charge to particle is so you know the Yang and.

632
01:32:07.710 --> 01:32:09.690
Daniele Teresi: Really short I mean.

633
01:32:10.680 --> 01:32:11.490
Leonardo Rossi: It may happen.

634
01:32:12.870 --> 01:32:20.760
Daniele Teresi: will happen in principle I mean for us by construction our articles have been type of one, so the decay angle, will be small.

635
01:32:21.240 --> 01:32:32.010
Daniele Teresi: I mean i'm just telling you this now because I learned about these results, last week, as everybody else right so when we wrote the paper we did not think about this or CBD, otherwise we would have written down.

636
01:32:32.610 --> 01:32:41.490
Daniele Teresi: Back in you know, then my understanding of that table is that these could point was an experimental lesion, of course, I do not have anything to say about that right.

637
01:32:43.680 --> 01:32:44.010
Thank you.

638
01:32:46.770 --> 01:32:48.330
Margaret Lutz: Thank you, then i'm not.

639
01:32:49.920 --> 01:33:01.680
Matt Strassler: Nearly I have the same complaint as Leonardo and and I don't think your answer works, because the boost you're talking about, for your benchmark model is about three and what determines the angles is gamma not beta.

640
01:33:02.280 --> 01:33:13.380
Matt Strassler: So, with a gamma three this particle is going to come out at a different angle it's going to come out with a different to PT it's going to have different curvature this thing is not going to be reconstructed very likely as a sinner try.

641
01:33:13.560 --> 01:33:14.490
Matt Strassler: us all the way through.

642
01:33:14.910 --> 01:33:27.180
Matt Strassler: And if instead you tried to say that okay it's it it decays to a particle of charged one and spits off something soft and somehow continues with the same momentum it's still it's going to curve differently.

643
01:33:28.860 --> 01:33:35.610
Matt Strassler: Maybe that you to go from charge to to charge one and it's a K and have the thing continue at a straight line or I should say and i'm on the same curve.

644
01:33:36.600 --> 01:33:44.850
Matt Strassler: This is just not very plausible you know you could try to do something by saying these things are had chronic and maybe their charge exchanging but then the problem is that.

645
01:33:45.630 --> 01:33:53.400
Matt Strassler: Again I don't see how that's going to be consistent, for these seven events with what happens in first of all in the TRT and, second, of all the immune system for these seven events but.

646
01:33:53.820 --> 01:34:00.690
Matt Strassler: It just does not seem to line up with a model like this, and on top of that, why don't we have events with two of these guys you're producing two of them.

647
01:34:01.260 --> 01:34:10.230
Daniele Teresi: So let me answer to both questions, so the first question is that we are talking about the to the order of that, and so what the experience of measuring is that in the radius of curvature basically.

648
01:34:11.280 --> 01:34:19.440
Daniele Teresi: up the order of the uncertainty, the mountain reconstruction is 100% or so we heard, we saw that and it's actually be mentioned these things.

649
01:34:20.730 --> 01:34:21.270
Daniele Teresi: So.

650
01:34:22.560 --> 01:34:33.300
Daniele Teresi: If you look at the momentum reconstruct and immune spectrometer the momentum in the fact that they are different, even for the seven events these was shown in the big tables, that was the second dog.

651
01:34:33.780 --> 01:34:44.580
Daniele Teresi: so well, I do not have anything blah blah right, these are not, I mean they're curious in the measurement of the moment, too much at least by it does not look enough.

652
01:34:45.150 --> 01:34:54.300
Daniele Teresi: to distinguish the two eyeballs is, but then I cannot be quantitative because, again, I learned that this week and to answer your second question, not.

653
01:34:54.780 --> 01:35:01.080
Daniele Teresi: A what happens to the other fact when we should take into account that all this process have an efficient, I mean all these.

654
01:35:01.770 --> 01:35:07.710
Daniele Teresi: Analysis have an efficiency of about order of 10% now clearly.

655
01:35:08.430 --> 01:35:19.140
Daniele Teresi: The efficiency for the two tracks and to reconcile the two tracks are not totally independent but part of the sufficiency is independent, in particular the one about the quality of the tracks, for instance.

656
01:35:19.470 --> 01:35:25.680
Daniele Teresi: That means that if you take into account that you already have this oppression which is 10% or 10% of.

657
01:35:26.010 --> 01:35:32.040
Daniele Teresi: The probability of heart of reconstructing to drugs is suppressed, as compared to the provincial reconstruction just one truck.

658
01:35:32.340 --> 01:35:42.840
Daniele Teresi: Now, since the access event is five or six or whatever we didn't not worry about this issue, of course, if the access events we become 20 or 30 in your with see one track.

659
01:35:43.350 --> 01:35:52.140
Daniele Teresi: you either have to invoke that this article, the case on slide to so, and so you have an additional suppression, even by the fact that has to leave for long.

660
01:35:52.740 --> 01:35:59.550
Daniele Teresi: Or you have to consider a model that that was mentioned in my talk in which the parent resonance is not the same.

661
01:36:00.030 --> 01:36:13.980
Daniele Teresi: As some sentiment of charge, so that indicates into two different articles one which has chargeable to talk and is the first one that will be seeing an experiment and the other one would be for recent chargeable one surgical to see it doesn't matter.

662
01:36:14.520 --> 01:36:29.520
Daniele Teresi: So you would see systematically only one of them, but these, we cannot tell with fiber lines, to be honest, because we have to dig it up on the fact that there is an additional suppression, there is an efficient so you'd have 10% in these these searches I don't know if that is the recovery.

663
01:36:30.480 --> 01:36:33.660
Matt Strassler: And I think the experiment is would have seen side to this but that's up to them to say.

664
01:36:37.500 --> 01:36:40.020
Margaret Lutz: No i'm and are your ad you want to.

665
01:36:40.110 --> 01:36:42.420
Margaret Lutz: chime in on that are Leonardo sorry I see your hand.

666
01:36:42.420 --> 01:36:51.240
Leonardo Rossi: yeah yeah no, I just want to comment that the most interesting thing I have seen in this this series of presentation is that.

667
01:36:51.840 --> 01:37:12.600
Leonardo Rossi: There is a matching between two of the seven events which, in my opinion, this gives a very, which is a as a very low probability of happening, it is clear, the only two events that is very as you get something that will be followed more more careful, so the two.

668
01:37:13.980 --> 01:37:26.700
Leonardo Rossi: Your position of to I bought this as as not to be thrown away, in my opinion, only because these two events which are certainly a very, very low probability to.

669
01:37:28.710 --> 01:37:29.130
weapon.

670
01:37:30.510 --> 01:37:31.020
that's all.

671
01:37:35.610 --> 01:37:38.100
Margaret Lutz: Okay, thank you, Stefan Oh, I see your hand go ahead.

672
01:37:40.230 --> 01:37:45.000
Stefano Passaggio: Yes, I agree with what you just said and I was wondering if.

673
01:37:46.110 --> 01:37:56.940
Stefano Passaggio: The model that has been presented, would be easily extendable to charge, so we have not too too bad fraction, for example, above one between one and two.

674
01:37:57.510 --> 01:38:12.570
Daniele Teresi: Yes, for us, yes it's not it's not a big deal, then, if you asked me this vertical motivation, or having a charge three half the motivation is much deeper but, of course, you know if you look at blue sky like you discover whatever you finding.

675
01:38:13.770 --> 01:38:21.630
Daniele Teresi: So there is nothing, you know that, but to be honest, if I look if I show you let me show you again my slide so.

676
01:38:22.920 --> 01:38:32.340
Daniele Teresi: I mean if I look at these uh you want to call it propaganda plot or whatever if beta is one and they initially is the one that you see from the pizza lead yaks.

677
01:38:32.940 --> 01:38:43.710
Daniele Teresi: The spot is telling you the charge equal to two is the way to go, basically, you can create you can do other lines for charge about 1.5 but then you would still have the the data problem, the problem.

678
01:38:44.190 --> 01:38:55.140
Daniele Teresi: is not going to be one so that's why we focused on can equal to basically because we want to reconcile that I unionization measurement so the needy X, if you seen that says.

679
01:38:55.530 --> 01:39:04.770
Daniele Teresi: With the information that i'm a slide that tells you that has to be close to one up to 0.1 or so basically so that's the real motivation for people to do that.

680
01:39:06.240 --> 01:39:13.200
Stefano Passaggio: yeah like actually concerning this plot, I have an additional question, which is a less relevant about.

681
01:39:14.250 --> 01:39:18.810
Stefano Passaggio: A spread of the pointer that you're showing here is not a Landau.

682
01:39:19.170 --> 01:39:20.730
Daniele Teresi: is right now is you.

683
01:39:20.760 --> 01:39:25.740
Daniele Teresi: know we are yeah we just repeat the calibration data that were provided by outlets.

684
01:39:26.880 --> 01:39:30.180
Stefano Passaggio: can look to see matrix somehow but maybe.

685
01:39:31.650 --> 01:39:37.200
Daniele Teresi: it's maybe we just feed us a crucible function, we see you know the.

686
01:39:39.180 --> 01:39:43.680
Daniele Teresi: The low energy data, which is the same procedure as others others does basically.

687
01:39:44.670 --> 01:39:45.630
Stefano Passaggio: Okay, thank.

688
01:39:45.810 --> 01:39:52.320
Leonardo Rossi: You any case disagree band if we will be charged to this event will be centered around sure.

689
01:39:53.580 --> 01:39:56.370
Daniele Teresi: Now this is beta not beta gamma he's been.

690
01:39:56.790 --> 01:40:12.270
Leonardo Rossi: Speaking about the dx the band the about the size of the of the vertical band say about the size of the band since Yun ization the the unionization in big cities normalized one.

691
01:40:13.980 --> 01:40:16.770
Leonardo Rossi: charge one feature to should be normalized too short.

692
01:40:16.890 --> 01:40:26.610
Daniele Teresi: And this is the blue line, this is the blue line is the dx the beta Blocker the Bank is what experiment sees Okay, the Web access events are I mean I.

693
01:40:28.380 --> 01:40:28.710
Daniele Teresi: Thank you.

694
01:40:31.050 --> 01:40:37.050
Margaret Lutz: Okay Thank you so much, I knew anything we have time for one more question, so I see on hand is raised.

695
01:40:38.790 --> 01:40:42.420
Jan Heisig: yeah Thank you so I wanted to come back to the.

696
01:40:43.440 --> 01:40:54.240
Jan Heisig: To the lifetime issue, so you you just entertain the idea that the thing could actually decay and then proceed via I mean proceed as a charge one.

697
01:40:55.920 --> 01:41:04.800
Jan Heisig: particle, and I mean Someone said that this would probably be or not not be possible, because I mean there are some some I mean.

698
01:41:05.670 --> 01:41:18.120
Jan Heisig: The track would probably not match, but I mean, is it really true that your is that sensitive to it, I mean if if the the charge one particle would not be super polite but say just half.

699
01:41:18.900 --> 01:41:30.210
Jan Heisig: The mass I guess with half the mass you would even if the same bending but even if it's not exactly half the mass just I mean considerably lighter I mean, would you be able to tell whether.

700
01:41:31.440 --> 01:41:37.110
Jan Heisig: I mean the two tracks match or would that be because of in this PT uncertainty is quite large right.

701
01:41:37.350 --> 01:41:46.440
Daniele Teresi: So I think not, because this is what you reconstruct I mean these curves is that you start from a particle instead of 2.2 times, where is the yellow line now.

702
01:41:46.710 --> 01:41:49.860
Daniele Teresi: So the physics here is just a single particle 2.2 times.

703
01:41:50.640 --> 01:41:57.600
Daniele Teresi: What you reconstruct then it's this broad curve that goes from 500 to four, five and more.

704
01:41:58.920 --> 01:42:10.440
Daniele Teresi: I mean I don't think it is enough curiosity to you know to to investigate these is possibility but then maybe the experiment is no better than me.

705
01:42:11.670 --> 01:42:13.500
Jan Heisig: yeah would be would be interesting.

706
01:42:15.540 --> 01:42:19.350
Jan Heisig: To hear from experimentalists about this issue.

707
01:42:23.670 --> 01:42:35.100
Matt Strassler: Well, if I may, that I mean you know if if the events that come out backwards, and it was the events that didn't show up in the immune system, which were also the ones that had low TRT.

708
01:42:37.080 --> 01:42:39.300
Matt Strassler: Then I would be plausible that's exactly the reverse.

709
01:42:42.180 --> 01:42:45.810
Matt Strassler: The events with low T or T shirt and immune system high TRT don't.

710
01:42:55.980 --> 01:43:03.780
Daniele Teresi: show up anywhere in charge, but one is enough to be seen in the immune system don't understand the point.

711
01:43:07.080 --> 01:43:13.740
Matt Strassler: The the events that don't show up in immune system are the ones that seem consistent with not having had the decay.

712
01:43:14.880 --> 01:43:17.340
Matt Strassler: they're, the ones who traveled far enough, that you still get high TRT.

713
01:43:18.510 --> 01:43:21.870
Matt Strassler: The events that have low TRT there, therefore, we have to be the ones to have the decay.

714
01:43:22.620 --> 01:43:22.860
If.

715
01:43:24.540 --> 01:43:26.730
Matt Strassler: Some of those might show up in the immune system, I don't disagree.

716
01:43:31.470 --> 01:43:37.110
Daniele Teresi: I mean depends on the decatur order, it is equal to one equal to zero right, this is our highly model dependent.

717
01:43:38.130 --> 01:43:47.970
Daniele Teresi: Statement right again, you know with this small number of events and the know how much we can I mean we just call it the physics right.

718
01:43:52.710 --> 01:43:53.520
Matt Strassler: I think i've said.

719
01:43:58.410 --> 01:44:00.210
Margaret Lutz: Something very quick yes.

720
01:44:00.270 --> 01:44:02.730
Leonardo Rossi: Very last one very quick so.

721
01:44:04.770 --> 01:44:23.280
Leonardo Rossi: Can you eventually look if there is any mechanism is such that we asked after a particle of say charge equal to two, there is a possibility of the decay product is a charged particle to one such that this is.

722
01:44:24.420 --> 01:44:26.820
Leonardo Rossi: say more or less cool being here.

723
01:44:30.030 --> 01:44:38.220
Daniele Teresi: But this is in winter is like an article question and the expectation is that is sufficiently polina just because beta is larger than 0.9 for us.

724
01:44:38.910 --> 01:44:48.210
Daniele Teresi: That is not going to be exactly the linear it's gonna be a Cone can be calculated that but, again, given the momentum resolution of 100% at this moment.

725
01:44:49.200 --> 01:44:57.510
Daniele Teresi: i'm you know my intuition tells me that this should not be a problem, but I do not have to do answer because you know these results came after our war.

726
01:44:57.570 --> 01:45:00.450
Leonardo Rossi: Now, because will be an inspiration and leveraging your.

727
01:45:00.930 --> 01:45:12.360
Daniele Teresi: mission, yes, to be honest, I mean for my face these are just a matter of space right the restore faith is large right we we have access to things like let's not try to do too much.

728
01:45:14.670 --> 01:45:15.930
Leonardo Rossi: don't do too much, but.

729
01:45:16.530 --> 01:45:16.920
Daniele Teresi: It was.

730
01:45:16.980 --> 01:45:18.210
Leonardo Rossi: Just a suggestion to.

731
01:45:23.610 --> 01:45:30.390
Margaret Lutz: Okay i'm nearly we can, if you have more questions, please put them in the matter most if you want to continue this discussion.

732
01:45:31.110 --> 01:45:46.740
Margaret Lutz: it's been very lively, so thank you to everyone who contributed, and especially to an area and Daniela for speakers, the speakers for making the physicians and, of course, everyone who made the presentations earlier in the day and then nearly if you could maybe post your slides so.

733
01:45:46.740 --> 01:45:47.820
Daniele Teresi: The server will.

734
01:45:48.060 --> 01:45:54.120
Margaret Lutz: will be great Thank you and thanks everyone for today and, hopefully, see you tomorrow.

735
01:45:54.690 --> 01:45:55.470
Daniele Teresi: Thank you bye.

736
01:45:57.480 --> 01:45:59.730
James Beacham (he/him): ciao everybody right.

737
01:46:01.740 --> 01:46:02.070
My.