Jennet Elizabeth Dickinson: Are in progress.
WH11NE- Sunrise: The new one and the chat platform, but, as you can see, are sensitively.
Jennet Elizabeth Dickinson: Is not.
WH11NE- Sunrise: One used to be a.
Jennet Elizabeth Dickinson: S- can somebody mean, you know.
WH11NE- Sunrise: Yes, a couple weeks.
WH11NE- Sunrise: and we'll see.
WH11NE- Sunrise: So, as I was saying, we have sensitivity to some of them, but most of them are way out of reach, including the Hicks South Public, which, as you're gonna see, is particularly interesting to study.
WH11NE- Sunrise: So why is the heats possible? So interesting? Well. The standard of his fossil is the result of electric breaking the mechanism responsible for giving mass to most part of the summer bubble
WH11NE- Sunrise: in the early universe. We had asthmatic potential with a minimum at 0. And then a few picoseconds after the view, and there was a fixed decision to announce 0 vacant expectation value E value and a several particles acquired. First, st okay, so this is what the standard model tells us.
WH11NE- Sunrise: It also tells us that this face transition should be of second order. That means it's a smooth transition, such that if we had a poll in the center of the potential it would smoothly roll over to the new minimum.
WH11NE- Sunrise: However, a 1st order of this transition would have also occurred. If, for example, we have additional beyond standard model, he exposens.
WH11NE- Sunrise: In this case we would have the the generate minimum as the critical temperature, and as the temperature reduced
WH11NE- Sunrise: is above, let's say, in the minimum, the potential would tunnel through to the new document, expectation, value.
WH11NE- Sunrise: and in theories, like remotes like electricity, party genesis.
WH11NE- Sunrise: This handling occurs through public creation, and is therefore capable of explaining the modern humanity.
WH11NE- Sunrise: This network gives us quite a good motivation about the potential existence of, for example, additional exposures.
WH11NE- Sunrise: So all of these, we're talking a lot about the shape of the ship's potential. So this is actually a quite clear door to to news districts
WH11NE- Sunrise: and measuring the depos of interaction is actually crucial to understand the shape of this potential. That is because when you look at the standard model prediction, so in the standard model.
WH11NE- Sunrise: this potential has to have a very specific shape for the electroicing to take place. So it does. It has to be this very typical Mexican mic
WH11NE- Sunrise: handshake. So at 1st order. This shape depends on the heap. Wasn't mass, which we know since the heap discovery at second order. It depends on the 3 4 hits of interaction that we hope to be able to measure at the lat. By observing the Hicks production, and it also depends on the quantity heaps of couplings. But we don't expect to be sensitive to this anytime soon.
WH11NE- Sunrise: So okay, we have this potential. It's supposed to be in a specific way, according to standard mobile. But how is it? Really? We are actually not so sure, so measuring. It will tell us about not only the nature of the electro weeks of magnitude breaking, but also will shed light about the stability of the universe.
WH11NE- Sunrise: Okay. So I said, we have the Hicks mass, that we know. But we don't know the triple H self coupling. So when you actually look into the experiment current experimental knowledge on the shape of this potential, what we know is actually baby little, there is a wide alternative of potential shapes
WH11NE- Sunrise: that are still compatible with experimental data. So there, there really is a lot of room for new physics here and there are different ways of research for it, both in address and Pms, and depending on the hypothetical masses of these new young particles.
WH11NE- Sunrise: So if we assume that new physics exists, and energy will be much, much higher than those produced at the Lec. We're gonna have to do a search, for for example, a a nonpresident search where we're gonna look into the capital framework and effective field series so that we see small deviations in the standard model of predictions caused by physics at a higher energy.
WH11NE- Sunrise: You said, it's okay to just
WH11NE- Sunrise: alternatively, if we assume that this new youngster model particles can be produced on the Lx, what we're gonna do is a bump search for these new particles.
WH11NE- Sunrise: So this is one of what I'm gonna talk to you about today. I'm gonna present 2 analysis, and both in the Vicinagama final state. And here I show you 2 of the event displays of the analysis that I'm gonna present. So in both cases the 2 columns represent the features from the Hix, and then the 2 towers represent the photons from the.
WH11NE- Sunrise: and you might wonder, okay, why do you become a gamma, and not a different final state? Gamma has.
WH11NE- Sunrise: mixes the relatively large branching ratio from Hicks to P. Depart. That allows us to have some relatively high statistics with the really small branching ratio, but excellent resolution of the Hicks to Kamakama. SDK, so this gives us a sweet spot, with the
WH11NE- Sunrise: one small branching ratio of CO. 3, 6
WH11NE- Sunrise: percent for the Hicks. So we become a kind of decay. But a really excellent resolution that allows us to have and quite good sensitivity to the process.
WH11NE- Sunrise: Okay, so let's begin with the nonprofit search. This analysis was published last fall. So you might have heard them about it a bit already, but anyway, I'd like to go relatively quickly over it.
WH11NE- Sunrise: So, as I mentioned an excess observed in the number of non-resident events.
WH11NE- Sunrise: And with respect to the standard, mobile expectations would actually indicate and use physics and higher skills. So that's what we're gonna focus on. I will focus mostly on the Kappa framework that I will explain in a minute. But I will also flash really quickly through the effective theory interpretations that we also publish for implement.
WH11NE- Sunrise: Okay? So in this plot, I'm showing you the Standard Mobile Production Cross section for a bunch of processes.
WH11NE- Sunrise: And as you can see here, the Hicks production has, like a section 1,000 times smaller, that signal, Hicks. So this gives you an idea of how hot it is actually to measure this process. It is very rare.
WH11NE- Sunrise: and in the standard model the leading production mode is low growth, mode, fusion.
WH11NE- Sunrise: Village is produced through these 2 appointment diagrams that you can see here. The top diagram that we refer to as the triangle diagram gives us access to the people heats up production.
WH11NE- Sunrise: And here, as you can see, we're representing the triple H interaction as capital lambda. This is what we refer to as the Kappa framework. So basically, what we are measuring is the coupling modifier. How much would the coupling and change on the real universe, and with what we would expect in the second model.
WH11NE- Sunrise: So in this case, Kappa Lambda and Capital P. Representedly the coupling modifiers of the Hicks of coupling and the topic of a coupling such that Kappa lambda and capital P equal to one and basically have standalone business.
WH11NE- Sunrise: So okay, we have these 2 final dynamics. And the first, st the top one is the triangle diagram.
WH11NE- Sunrise: and then, as you can see here in the mh, distribution populates mostly the software, mh, etc, while the box diagram is a populates mostly a hardware Mhch distribution that you will see here in red
WH11NE- Sunrise: both and diagrams, and that, and resulting in some distractive interference. And the that is the reason why the the Hicks production perception is so small at only around 30 September.
WH11NE- Sunrise: So I have told you that.
WH11NE- Sunrise: And there is interference between both diagrams. Therefore, when we actually modify Kapa lambda to have something that is not standard model like, we will also be modifying this interference part and pattern, and therefore.
WH11NE- Sunrise: having capital lambda values so kicks off coupling, not standard like, will actually affect both the heaps, production, perception, but also the Me. Chiefs distribution. So here you can see how, for example, the production is minima for a couple lambda of 2.4 5
WH11NE- Sunrise: that you can see in the Mhc distribution setting go that results in maximum and shape pattern. And in this analysis we don't consider only Ggf production as signal, but also the subbing products of
WH11NE- Sunrise: and the Soviet production world effective also fusion that has an even smaller recession of 1.7 petrol.
WH11NE- Sunrise: So at the end of the of the day we have 5 final diagrams that we consider as single for the Higgs production. And again, here I'm showing you the cup of rain. So we have Kappa B and Kappa B, which are basically the coupling multipliers that we know very well and with position within 10% from single Higgs. But then we also have Kappa and Capital B, where there is an interaction with 2 exposures, and we don't know this, so well.
WH11NE- Sunrise: Therefore here is where we are more interested on searching for potential pr standap physics.
WH11NE- Sunrise: Okay, so we have a we become a gamma final state, which means that our event selection consists on using the photo triggers and to select the 2 photos coming from the heath to gamma gamma decay. And we use we do attack on the dipotane mask between 105 and 160 Gb. That is a comfortable.
WH11NE- Sunrise: relatively large window around the dipotane mass, and you will see why it needs to be large in a moment, and then we also select, require 2 beaches, obviously from the heats to the barbecue, such that both environment muscles equals to the heats
WH11NE- Sunrise: and the like. Photon digestion divided mass distribution will basically reconstruct the digit department.
WH11NE- Sunrise: And we also have a laptop video, and we've acquired at least, less than 6 central jets, as a way to suppress the Vthal backgrounds, and in particular, in particular, for Pdf. Signals. Since we have 2 additional jets we use boosted decision tree to decide which of these extra jets that we have that are not the ones that are actually coming from the Vps production.
WH11NE- Sunrise: Okay? So then, we said that, and we become a grandma gives us the Mh distribution. So therefore we can use this distribution to search for both standard model and Bsm signals.
WH11NE- Sunrise: And we apply a cat at around 350 TV. And we train to decision based.
WH11NE- Sunrise: So for high mass, we are targeting further model. And we end up defining 3 categories and for low mass, we train Vs against background. And we end up defining 4 categories. All of these categories are then used to be the dipotom spectr. This is very often done for Higgs to come and come analysis, because we can do an Mp. Where we use an exponential distribution to predict the
WH11NE- Sunrise: continuous background and the double side crystal ball to very cleanly fit.
WH11NE- Sunrise: the mama gamification from publishes.
WH11NE- Sunrise: So in particular, these categories, as you can see here, are basically just teams in the in the native distribution.
WH11NE- Sunrise: And with this, how do we reach, the results? So this analysis has this leading uncertainties, both data statistics, but also the theory uncertainties on the screen section, and it says, upper limits to the single thread modifier for Gdf plus Pbs. Production
WH11NE- Sunrise: below 4 for the observed limits, and 5 for the expected limits. That means that if the the hick selection perception was more than 4 times larger than that expected by the standard model, we would expect to have seen this already. We also send limits to capital Lambda, so to the deviation from the Hixson and Kappa 2 B.
WH11NE- Sunrise: So this analysis is done only for to become a gamma. And it takes into account. It considers that the young standard model of text are actually limited to the coupling modifier that we are probing.
WH11NE- Sunrise: We also have fresh from Ltp. The combination, the numbers and the combination from address. So in this case we are combining a bunch of final states. And as you can see, this is proof that I wasn't lying. So if you become a gamma is one of the most sensitive final states, so as you can see here when we look at the single sign, Qualifier vivica has
WH11NE- Sunrise: the tidies the lower second limit we offer. Oh, but maybe downtown has a lower expected limit. And when we look at capital lambda and is the absolute winner, we have the the tighter constraints. And
WH11NE- Sunrise: so, yeah, and that is mostly because Vidicama is actually software. So you can see here at lower masses that the purple from video flow as the green from Tidicamagama are relatively close together, however, higher values, which, if you remember, we're more. Psm, right? We actually have the purple from Vidicamagama supplied.
WH11NE- Sunrise: and better, much better than give it up is because the gamma is sensitive to software energy distributions, and therefore through these larger capital under bodies. And okay, so this is what we would have for our couple of framework and combinations. But I mentioned that we also have effective for theory interpretation. So I'm gonna go through this very quickly, just because there isn't
WH11NE- Sunrise: that much time. But the idea here is that the effectiven frameworks allow us to make a more general manner of the heat stop happening.
WH11NE- Sunrise: So here I'm showing you the general he's effective to appear or have where the he is considered to be a secret. So it's quite a general framework.
WH11NE- Sunrise: I like it because it's really a new exception of the type of paperwork such that you still have the standard model diagram
WH11NE- Sunrise: you just change from couple lambda to see HA. You change the name of the company, but you also have 2 new, final diagrams that give,
WH11NE- Sunrise: And in heck.
WH11NE- Sunrise: because this is because the heap is a singlet. We are actually also particularly interested in measuring this Cth and GGH. Couplings, since we will expect the heaps production to be the only sensitive channel for for these processes.
WH11NE- Sunrise: And we expect both of these to be, you know, in in the standard model. So this is something that we are also interested on testing here. And but the beauty of this ef interpretation is that we don't need to constrain our study to only one coupling. But we can actually do a proper parametrization that is more physical and study things
WH11NE- Sunrise: and study the SMS on multiple couplings and processes simultaneously. So this is what we do with the half benchmarks that are also widely used by Cms, and we study how changing multiple couples values will affect the mit distribution. We ended up curious ended up with a different set of shape benchmarks that represent
WH11NE- Sunrise: the multiple shapes that keep repeating themselves while you have different combinations. And what we do is we set limits to these benchmarks. So the interesting thing about this combination plug that you have here on the left is that we are less sensitive, as you can see here to benchmarks one and 2 that basically represents. And
WH11NE- Sunrise: using this with software image spectra. So that's something to take into account, and that we should try to to improve.
WH11NE- Sunrise: So this would be for heft. But of course, we also, and performing the traditions in the more popular and Smith
WH11NE- Sunrise: effective your theory. So, Smith, you probably recognize it from the global feed that are performed in Adelaide and Cms. So in Smith assume that the heat is actually adopted. So, therefore, has much more constraints, if it's single heats, and the heat, and therefore it's especially useful for combinations at this point. Others combinations do not include this net ch. And with superstition, which is the one that is especially sensitive
WH11NE- Sunrise: to the heat, and we are working on measuring these couplings and implementing it in our analysis, as you can see here from the PVC camera as well. We already submitted to Ch and Ch box.
WH11NE- Sunrise: So we have 5 snap couplings that we are sensitive to in the heats. But these 2 are the ones that we are most sensitive from so combining. Being part in the close, it will be beneficial for the heats, but also from the other processes that we can get.
WH11NE- Sunrise: And the 2 couple things that I mentioned.
WH11NE- Sunrise: And I'm yes, I'm live from half in this, and we are finding that data, at least this time is most likely 0.
WH11NE- Sunrise: Okay, so with this, we're done with the nonreson analysis, and we move on to the search that I find a bit more fun. Personally, I bias. Okay, so this is what I call the baby of my Phd, by the way, so I'm I'm really my
WH11NE- Sunrise: anyway. So in this case we are doing a bump search for beyond standard mobile particles with an unknown mass that we expect can be produced at the Fc.
WH11NE- Sunrise: Previous searches of this type consisted on searching for a heavy color. It became to do standard model like. So we had this sort of plot, and and no use case was found. But we see that from the female sensitive channels again, and Vivica Magama is again most sensitive. You see here in this to lower masses
WH11NE- Sunrise: and lower Mx. Heavy scalar masses. Why, 4 B would be more sensitive to higher masses and give it out would be more the most sensitive, and then is the needed regime.
WH11NE- Sunrise: So this is cool, but it can be more fun if, instead of considering that you are decaying to 2 standard model pieces. You allow one of the pieces to not be standard model like, but just to be an other beyond standard model heats. So in this case we are doing a modeling search or beyond standard model exchange.
WH11NE- Sunrise: such that and the heaviest is Px. And then they have. So the difference from the previous analysis is that again, before the mass equals to the Santa Monica pigs, Mass.
WH11NE- Sunrise: Mv. Equals Ms. Which might be 135, or might be something else in principle. If it was actually 135, we would be going back to the previous analysis that I mentioned. But in this case, the diversion digit mass is, gonna give us a resonance hopefully. If this new physics exists and we can find it in the latency.
WH11NE- Sunrise: So okay, what masses? Exactly are we looking for
WH11NE- Sunrise: here? You can see it says 12 parameter space by any analysis, several of which are from Cms.
WH11NE- Sunrise: So as you can see here in blue, is the analysis that I'm presenting to you today from Atlas in red you have the analysis in the same final state, but from Cms. And then here at large values, you have other analysis, like, for example, the 40 final state, which, as we said, is
WH11NE- Sunrise: so in particular. This analysis is the only one that tries to go to really low masses. Because we thought so. That's actually how we started. And we only expanded to higher masses just to allow combinations later. And so basically, we are the only analysis that assumes a Mx below and 250 jb, which would be the mass of the 2 pieces.
WH11NE- Sunrise: Have a mother like explosives?
WH11NE- Sunrise: Okay, so how does our signal look like
WH11NE- Sunrise: photos? Look exactly like they would in the north as an engage. You have a hiccain to do. Photons with the same type photo invite, and last window.
WH11NE- Sunrise: but the to be jets from the S. They might do different, depending on which regime in which regime our signal is. So we have some boosted signals
WH11NE- Sunrise: for part of our business where the
WH11NE- Sunrise: Egypt ends up, the big parts end up in the same and each end code when we use our standard attacking algorithms. So this would be our boost regime, where we can only reconstruct one feature.
WH11NE- Sunrise: And then we have our resolve emails where we can possibly reconstruct both of the features in the event.
WH11NE- Sunrise: So basically, we define 2 preselection, the one we checked and the 2 big reselection. And we decided how to divide our face space just looking about which resolution was giving us a high efficiency for each of these signals. So we ended up with this one bill line of signals that are actually boosted, and therefore are targeted in the one we check delivery. So this is
WH11NE- Sunrise: one B as a function of Mx. However, for the result case, we have a 2 degree of Mx. And X monthly that we are talking.
WH11NE- Sunrise: So we are targeting a lot of masses. And our question was, How can we do that. And how can we do that? We had some concerns that we were quite obsessed about. The 1st one was we needed to be careful not to leave any sensitivity gaps.
WH11NE- Sunrise: and I wear a present signal and put high. I had previously worked on suic searches where signal is never as resonant. It's mostly growing. So then you just interpolate the limit, and you're fine but here we're searching for residences. So this was a very important concern. Also, we wanted to really know if we found an access. We wanted to be confident and be able to say what signal had to use such excess.
WH11NE- Sunrise: So this is something that we were also trying to achieve. So the solution we found was parameter networks.
WH11NE- Sunrise: So the idea here is that instead of teaching a neural network how to discriminate each signal independently so that we could have
WH11NE- Sunrise: a bunch, a bunch of neural networks. I don't tell you exactly how many you would have needed or using a neural network where we feed all of the signals. And we just ask you to stimulate all signature background. What you do is with this the masses as a parameter to the neural network.
WH11NE- Sunrise: This way a single Pm. Or in our case, because we will have one for each region, will be able to discriminate signal for background as a function of the parameter that we are targeting. If this also allows us to use all of the
WH11NE- Sunrise: all of the and all the current events that we have for single together, helping the the network and learn better.
WH11NE- Sunrise: So at the end of the day we have, as I said, 2 pns for the 2 detached region. We use Ms. And Mms. As parameter, so we have a 2D. Parameterized into the for the one feedback pnn. The parameterization is performed only on one day, and we have very few training variables. This is a very simple parameterized network. So for the 2 detached region. We only use Mvp. And Mvac for training, but it is actually enough.
WH11NE- Sunrise: as you can see here in this plot to disseminate exactly the signals that we want from Backup. So here I'm specifying that I'm targeting the 200 5,100 Gb. Signal. That is the solid blue line that you can see here picking towards one as it should.
WH11NE- Sunrise: and all of our microcosm toward 0.
WH11NE- Sunrise: We also see that when we plot a signal that is a bit far away from the targeted masses. We also have it taken towards 0, so we could easily be able to differentiate in between signals, but when our signal is relatively nearby, so we don't mention that you be our Mx. Value. We have a more flatter distribution. So this gave us confidence that we would not be missing signals.
WH11NE- Sunrise: Okay? Yes. Question. So if if these are the only variables that you need to identify your signal, why do you need a neural network, you can fit both Mvp. And Mvp. Camera.
WH11NE- Sunrise: So, my colleague was working on the To on the To the and the pnn is just has to do the better everyone, especially at higher masses, because you run out of background. So then you don't have enough statistics to to do what to the 5.th So when you were going to masses above 600 Gb. More or less for the next.
WH11NE- Sunrise: there was just not enough statistics, and the analysis became a cut account analysis.
WH11NE- Sunrise: So that's where specially just getting the 2 display. All mixed up together in the Pnm is is beneficial. And but also in the in the main part of the space. Yes.
WH11NE- Sunrise: have a question. So
WH11NE- Sunrise: why is your agent signal like picking? As if because of background, I would assume that it would still be towards one.
WH11NE- Sunrise: I think, especially for 2 50 Lady X. And that 100 Gds. Still pretty close to each edge.
WH11NE- Sunrise: It is not because, Mvgamagama, you expect the resident peak that you don't have for non residential.
WH11NE- Sunrise: So for Mv. In this case you are asking the Ms. To be 100 Gb. Well, it would be, of course, 125 Gb. For the heat, so Mvp. Would not be too far away. I agree with you.
WH11NE- Sunrise: but Mx is would be different. You would expect that we could clear Peak around 250 Gb. While the Mhf. Distribution in the numbers and other analysis.
WH11NE- Sunrise: We have same good looks.
WH11NE- Sunrise: Thank you. Just updates them.
WH11NE- Sunrise: So it's not a clean resonance like we're seeing for the
WH11NE- Sunrise: or the other. You will also see, I'm sure you have more questions about why this will need to worry about, but you'll see that there is also another reason.
WH11NE- Sunrise: So I'll I'll get to that in a moment.
WH11NE- Sunrise: Like, what's I?
WH11NE- Sunrise: Okay? So I think it's like the sun. Any other questions about this?
WH11NE- Sunrise: Okay, okay?
WH11NE- Sunrise: So we have trained the parameterized neural network and
WH11NE- Sunrise: M
WH11NE- Sunrise: depending on all of our events at this election. So, as you know, and we have the dichotomy mass that usually we use for fitting in Key Sudan analysis. This is not what we did here.
WH11NE- Sunrise: And so what we did is we defined our single vision. Applying a window around where the photo divided mass is around the Hex, mass. So 120 to 130 Gb. And we consider that our single vision and we can, and we take the sidebands
WH11NE- Sunrise: of the dichotomy and mass distribution as our contribution. So what we did to make sure that our DNA was fully predicted. You know it had not only learned the Monte Carlo, but it would also predict the data is we studied the agreement of data, Monte Carlo in the
WH11NE- Sunrise: parameterized neural network for all of the parameters that we were considering to pro. And we saw that the agreement was quite good for digital. And then the idea is that we use the parameters neural network distribution in the single vision to actually extract, to do our likelihood and extract our signal.
WH11NE- Sunrise: So we perform this likelihood fee fit to the Pnn distribution for each of the master offices that we're serving.
WH11NE- Sunrise: But what exactly are the signal hypothesis that we're probing. I told you that we were really concerned about not missing any signals, so we perform some signal injection tests, and concluded that we had to pro a total of 359 signal hypotheses in the face date that we were considering in order to confidently say we would not miss any signal
WH11NE- Sunrise: just to give you an example, as far as the granularity that we were considering was 35 Gb. For Mx. Which is what you have in this file. And then the signaling checkion test showed us that here we could actually drop one column and one row, because at higher masses. The
WH11NE- Sunrise: well, the resolution is, is higher. It's a wider distribution, and therefore you don't need such and small granularity. So okay, getting the shape for the background for any of these. You tell it which parameters you are targeting, and for the signal we actually needed 359 signal. That's not what we generated for 3
WH11NE- Sunrise: as we could have just done, you know, €859 to start with. So we really wanted to find a way to interpolate, the signals? So that's where the very simple buyer was coming.
WH11NE- Sunrise: We have been able to interpolate the signatory, please of the to be tagged variables in the 2 to this to each and region. So Mamma Gama, Jj. And Mg, we can bring them to the
WH11NE- Sunrise: center of mass claim, and then to some Lawrence transformations in order to predict how this distribution is gonna do for all the other classes.
WH11NE- Sunrise: And then, when we have these 2 variables
WH11NE- Sunrise: or the interpolated templates from these variables, we can feed them to the correct regional network to predict how the Pnm and distribution is gonna look like. And as you can see, the simulation simulated, template and interpolated templates are actually quite in agreement. But this is not the case everywhere.
WH11NE- Sunrise: So the point hearing thing, is where we managed to get our interpolation to to work properly. The agreement was good, and therefore we could use interpolation templates. But it is lower areas. It didn't work.
WH11NE- Sunrise: We could never do it with the quantity tax addition.
WH11NE- Sunrise: just because we use also more variables. And yeah, therefore, we manage to that. We use quite a lot the amount of signals that we have to generate. But this is something that we would like to improve for the future.
WH11NE- Sunrise: Okay, so
WH11NE- Sunrise: with this, I reach to the results. In this case the living uncertainty arises from the modeling of the largest markets. The camogram of trust jets. Because in this case we're not doing, we're not doing a big, driven estimation from this background of the catalog. So we have quite a lot of
WH11NE- Sunrise: and limitation in the statistics for the other 7 people. Chicago here, and the normalization of the background is corrected, using the site plan as what we in the final fit. But here you can see the unlimited signal regions with the background on the face for 2 points where you see that the agreement is quite good between and data and background.
WH11NE- Sunrise: and when no new physics was found here, however, we did find the very full excess and an Mx. Of Icon, 75 Gb. And Ms. Of 200 Gb, so this is a display that I've been showing you, is actually the data point with the highest pnx. Port distribution, from this point, and as you can see, the photo, instead of being more
WH11NE- Sunrise: close by, like, it's usually the case, we have like one photo going in each direction.
WH11NE- Sunrise: So here in gray, you can see that how the signal would look like. And you have 6 events.
WH11NE- Sunrise: 6 day event over the background. Expectation in the in the last day.
WH11NE- Sunrise: But how does this translate to insignificance? Well.
WH11NE- Sunrise: here you can see the observed significance plots for our analysis, and, as you can see, we have a very beautiful excess here with this diagram like shape. So the success
WH11NE- Sunrise: looks exactly like signal injections testing. So we are quite confident that there's something
WH11NE- Sunrise: wrong with this. And, as you can see, we have a local significance of 3.5 standard deviations. And if we go back to our status that would indicate evidence for new physics, except for the fact.
WH11NE- Sunrise: then we're looking at 359 points.
WH11NE- Sunrise: So the probability to find a statistical fluctuation when you look at so many and in so many places is actually relatively high. So we need to take in to work out number of trials that we are, doing
WH11NE- Sunrise: so which a and this is what we use to calculate the global significance which reduces our significance to only 2 standard agents. And therefore it is absolutely no significance. Pretty standard model, like.
WH11NE- Sunrise: So after calculating the global significance. We move on, and we set upper limits to possible masses for the X and SA. Exposes, and this as limits as a function of the cross section time spreading pressure because we are trying to stay as modern agnostic as possible.
WH11NE- Sunrise: And as you can see, the linkage vary between 39 times over and lower masses, but we are less sensitive.
WH11NE- Sunrise: 2 0 point 0 9 5 at at higher buses.
WH11NE- Sunrise: And if you're familiar with the counterpart analysis from Cms. You might ask me, what about the excess the Cms found here? So Cms. Has you? Cms. Has an excess at 600 Gb. For the next 90 Gb. For Ms. With the significance respectively of 3.8 and 2.8 sigma. So this is actually a real excess of like hours of computers with the
WH11NE- Sunrise: with the tale tactics.
WH11NE- Sunrise: And and you said limits and a certain type of limit of the transactions expansion ratio for this point of 0 point 6 cent of us.
WH11NE- Sunrise: So in advance, we do not see any excess here, and we actually set a limits of concentration time spanning ratio of 0 point 1 7 3 2 0 point 2 10th of our 0 point 15 tent. And this point we actually use interpolated templates. So that's why we have computed this twice, just to make sure that was not an issue
WH11NE- Sunrise: with our high uncertainties arising from the temporated templates. But we we do not sometimes meetings that with multicolon or with interpolated templates.
WH11NE- Sunrise: Okay, so with this, I'm reaching the end of my presentation. So and I wanted to talk to you about prospects for Run 3. As you know, we are already collecting data from Brad and 3. We expect 2,000 available for Run 2, but also in Atlas we have improved quite a lot. Our,
WH11NE- Sunrise: Italian algorithms, they have a much better rejection. And for the same efficiency. So this will automatically be translated to all of this the heat analysis, because, as you saw, all of them require, or most of them require.
WH11NE- Sunrise: at least one huge thing to give you back, so we really expect that there will be an improvement. And here I pulled some numbers, done by a colleague some time ago.
WH11NE- Sunrise: So we expect that standard model expectation for the his production could be within range 4 and 3. Assuming more or less the same phase of analysis that we have observed in the in previous rounds, which was a factor of 1.7 and all combination with Cms. So we will see if that's the case in in a few years, and just
WH11NE- Sunrise: to conclude, the existence of an extended family of the exposure is supported by several beyond the model estimates. It could explain the matter and the matter similar in the universe, it could shed light about the stability in the universe.
WH11NE- Sunrise: Well, both addresses and Cms. Perform searches, and for this articles assuming different hypotheses and and the case for now we have no evidence of yes, and he's awesome. But there's a white face space that still needs to be broke and a lot of more data.
WH11NE- Sunrise: So maybe we, we would like it to
WH11NE- Sunrise: thank you.
WH11NE- Sunrise: Okay.
WH11NE- Sunrise: questions.
WH11NE- Sunrise: why is this a search only for Hs, why does it have to be an S. Why does it have to be a scalar? It could be a vector
WH11NE- Sunrise: anything that goes to?
WH11NE- Sunrise: And
WH11NE- Sunrise: are your results reinterpretable for?
WH11NE- Sunrise: Yes, so
WH11NE- Sunrise: fortunately, I don't think we have looked into interpretability yet. For other models. So we what we do is search. We do interpret model model. But for now we are giving our diagnostics, and in the future we might we might do more checks. But we, we haven't considered what we have considered, though, is flipping the the.
WH11NE- Sunrise: So that's something that
WH11NE- Sunrise: you do, and into.
WH11NE- Sunrise: And we might, we might do for the future.
WH11NE- Sunrise: Yes, Hi, thank you very much for the nice presentation. I have 2 questions. The 1st one is, what branching ratio do you assume for the
WH11NE- Sunrise: so when we we don't assume any branching ratio, when we generate the samples, and by default we assume that the model, like branching ratio when we do our status. But at the end of the day we're setting limits on the 1st section. There's branching ratio. So, people
WH11NE- Sunrise: I was. I was getting there. So what would happen if you weren't to measure the Steno Gamma
WH11NE- Sunrise: resonant analysis? But would you get the same sensitivity as to not resonate?
WH11NE- Sunrise: And sorry I didn't follow that. So in the resonance analysis, you also have a point of 225 right? It corresponds to the previous analysis that I mentioned. Would you also have resonance?
WH11NE- Sunrise: So the language was missing. Here is this one?
WH11NE- Sunrise: Yeah. So what I'm saying here is that the face space is the same as the non resident.
WH11NE- Sunrise: My question is.
WH11NE- Sunrise: oh, optimal, is the analysis so so do you have a comparison like you check like, if you were trying to measure. No, no, we would not get competitive at all. But just because of how these analysis are designed, right? Like, you use the same pre selection.
WH11NE- Sunrise: So in principle, you're starting from the same thing. But in one case you're taking boosted decision trees to actually find the other model like signal, while in the other ones you're training new networks abilities to target the residences. So you are really, really not. But for example, this this analysis that I'm showing you here, both at the same time as the non resident one. They are like signalling analysis. And
WH11NE- Sunrise: yeah, this is just not not sensitive to to the other. But I don't think we completely.
WH11NE- Sunrise: We could use the phone numbers.
WH11NE- Sunrise: Yeah, I I maybe I missed. I I want to understand more how you do the background estimation with the Pnm. So because it's parameterized, you rerun the background estimation, procedure for each signal hypothesis, or you do something else.
WH11NE- Sunrise: So you just produce Mvp, and then you become a gamma for the background. It's just one piece of it doesn't change right for the signals. It's gonna be a different distribution. So that's why you needed that. And you say, Okay, now give me the parameters for like now, and it gives you one sheet, and you have to predict the background.
WH11NE- Sunrise: But you're always getting the same piece of the reps all the time. You just said it. Okay? And because they are always picking towards 0. But at the end of the day it's the same on the table of the same system that you're seeing to the Vietnam 359 pounds, and then we need to tag. I think that's
WH11NE- Sunrise: but for the signal you do need to give it a different distribution. That's why we needed to generate. And, for example.
WH11NE- Sunrise: so correctly. You lower. You keep M. Gamma Gamma at the Higgs, Mass. And you scan Mvp.
WH11NE- Sunrise: Yes.
WH11NE- Sunrise: why not? The other way around.
WH11NE- Sunrise: Yeah. So this is something that we can fill up at the beginning. And this is mostly historical reasons.
WH11NE- Sunrise: So and
WH11NE- Sunrise: sorry.
WH11NE- Sunrise: So we would naively think that this background rejection for Low mass will be better, for when you're approving an analysis, you're not supposed to say these things, but you know the reality exists. So when we started this analysis, we were actually doing, we become a gamma, because we thought, this normal space has not April. So when we we did the study about which final state how we want to communicate in estimation. And here
WH11NE- Sunrise: our decay s to be and kings to photo was actually more sensitive. So that's how we started. And then, once we started developing the strategy, we were told, hey, by the way, we have several message, why don't you increase also even more sensitive? Why don't you increase the face phase so that we can eventually combine with the other processes? So at this point. We already have finances, strategy, and we didn't switch. But previously, what we have thought is.
WH11NE- Sunrise: if you flip the decay, then you're more sensitive in this region.
WH11NE- Sunrise: So our our 1st study suggested that platform is lowered us, and to be me was more sensitive than kicks to
WH11NE- Sunrise: idiot.
WH11NE- Sunrise: But in the future we are looking to the possibility of explaining these countries.
WH11NE- Sunrise: Okay, so
WH11NE- Sunrise: at letting the lower mass resonance go to the E is
WH11NE- Sunrise: more sensitive.
WH11NE- Sunrise: I think it the lower month. License, if not the lower, So
WH11NE- Sunrise: when you consider Mx and Msa, if you consider this part of the Facebook, yeah.
WH11NE- Sunrise: the process that we're targeting is more sensitive than they participate.
WH11NE- Sunrise: But it's worth our studies on the temperature
WH11NE- Sunrise: at the very selection we were using.
WH11NE- Sunrise: That's surprising to me. But okay, cool. And can I just look at the Catalan plot again?
WH11NE- Sunrise: Combination one. Yeah.
WH11NE- Sunrise: it's a lot going on there. Yes.
WH11NE- Sunrise: I appreciate the discussion about how different channels have different roles in the 2 figures because of the mass dependence.
WH11NE- Sunrise: And it looks like we're ex excluding the standard model at one signal. It's not very exciting, but
WH11NE- Sunrise: am reading that correctly, we're excluding this. I we're not excluding standard model at once. Not yet.
WH11NE- Sunrise: I didn't say not yet.
WH11NE- Sunrise: Alright. I just want to say that a second time. Thank you. Yes, and we are also very careful to to not say to Confucians.
WH11NE- Sunrise: If you're interested on this I also have in the backup the capital one.
WH11NE- Sunrise: I have a lot of items.
WH11NE- Sunrise: Okay, so here you can see the Kappa 2 B instead of capital lambda. So here it's actually the Pdp analysis that is the most cactus.
WH11NE- Sunrise: and it is especially because,
WH11NE- Sunrise: Yes, and values of capital. Pf, but they have a more boosted signal. And yeah, for for me, we have a boosted the management.
WH11NE- Sunrise: and then you can also see the capital E to capital lambda in the in the combination.
WH11NE- Sunrise: Would you see that actually, if you have a gamma.
WH11NE- Sunrise: if you will.
WH11NE- Sunrise: here's a part of Canada.
WH11NE- Sunrise: So in total, here we become. A camera would be the most sensitive. But then for the couple to be for the
WH11NE- Sunrise: months excellent
WH11NE- Sunrise: commercial practices have touch marks.
WH11NE- Sunrise: So I wanted to understand
WH11NE- Sunrise: for these benchmarks. So usually I use the same.
WH11NE- Sunrise: This. These are the other ones also. So
WH11NE- Sunrise: usually I'm used to seeing
WH11NE- Sunrise: is this because there has
WH11NE- Sunrise: so the reason for this benchmarks? This is done specifically in heck, because, since the, as I said, since the heats is a singular. There is actually a lot more room for the physics specifically coming towards eye heat.
WH11NE- Sunrise: And I've come by 6.
WH11NE- Sunrise: So we just here want to get an idea of each distribution if you're not setting limits on this particular couple. And, for example, here you have the. Here's prediction. I'm not a fan of the theory prediction here, because the theory prediction comes from this specific set of counting so represents a shame. But one of the things I was part of this working group. Note that there is this recommendation, and one of the things we did is
WH11NE- Sunrise: the benchmarks that we're available from series in the past were outdated, they had already been excluded those points, but the shape is not excluded. So here, what you are trying to understand is all of the combinations of these couplings that would give a specific.
WH11NE- Sunrise: But we also do
WH11NE- Sunrise: like it would be. It's on the test parameters
WH11NE- Sunrise: here, and eventually the Api is, of course, correlation to start to increase what data center.
WH11NE- Sunrise: But we, we're getting it.
WH11NE- Sunrise: This is easier for health, because there is no dependence between singlets and banking.
WH11NE- Sunrise: It has been smack. You have to take into account all the organizations, the backgrounds as well.
WH11NE- Sunrise: Actually, if I answered your question.
WH11NE- Sunrise: I can go ahead. Okay.
WH11NE- Sunrise: I'm around those
WH11NE- Sunrise: question. No
WH11NE- Sunrise: sorry is around all day. So if you want to talk to her, she's in my office.
WH11NE- Sunrise: go about the next.
WH11NE- Sunrise: So much for coming. It's been a lot of money.