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BOOST 2019 is the eleventh conference of a series of successful joint theory/experiment workshops that bring together the world's leading experts from theory and LHC experiments to discuss the latest progress and develop new approaches on the reconstruction of and use of boosted decay topologies in order to search for new physics.
This year is hosted at the Stata Center at Massachusetts Institute of Technology.
The conference will cover the following topics:
Previous editions:
We present tools developed by CMS for LHC Run II designed for pileup mitigation in the context of jets, MET, lepton isolation, and substructure tagging variables. Pileup mitigation techniques of "Pileup per particle ID" (PUPPI), and pileup jet identification are presented in detail along with the validation in data.
In order to study hadronic final states, it is of utmost importance to consider the inputs used when building jets, and the definition of the jet reconstruction procedure. These fundamental choices of how to build jets have wide-reaching implications, from pileup stability to the precision of the resulting jet energy scale to the ability to tag and identify hadronic decays encapsulated within the jets. The inputs to jets and jet reconstruction procedures used by ATLAS will be discussed, as will the subsequent implications of these choices on hadronic physics.
The physics reach of the Future Circular Collider in hadron mode (FCC-hh) with a centre of mass energy of 100 TeV and unprecedented luminosity has been studied and published in a Conceptual Design Report (CERN-ACC-2018-0058). In order to exploit the full physics potential of such a collider, a conceptual detector design has been developed and tested in fast as well as full-simulations within the common software framework FCCSW.
The discovery reach for new heavy resonances, like Z' or graviton decaying into bosons or top quarks, highly depends on the performance of the detector system. Their signals occur with a strong boost in the central region of the detector. The successful reconstruction of e.g. $Z'\rightarrow t\bar{t}$ especially depends on the calorimeter granularity, necessary to distinguish the three body topology of the jets sub-structure from the QCD background.
The reconstruction of boosted, and highly energetic jets sets the calorimeter performance requirements in terms of shower containment, energy resolution and granularity.
We will present the performance of the FCC-hh reference detector in terms of jet energy resolution, discuss the challenges of a 100 TeV proton-proton machine and show first results of jet sub-structure studies, that use multivariate analysis techniques to distinguish boosted W and Z bosons from QCD jets in full FCCSW simulations. The results will be compared to results of fast-simulations using the Delphes package. Finally, the prospects including particle flow algorithms will be discussed.
Jet substructure variables for hadronic jets with transverse momenta in the range from 2.5 TeV to 20 TeV were studied using several designs for the spatial size of calorimeter cells. The studies used the full Geant4 simulation of calorimeter response combined with realistic reconstruction of calorimeter clusters. In most cases, the results indicate that the performance of jet-substructure reconstruction improves with reducing cell size of a hadronic calorimeter from $\Delta \eta \times \Delta \phi = 0.087\times0.087$, which are similar to the cell sizes of the calorimeters of LHC experiments, by a factor of four, to $0.022\times0.022$.
Hard scattered partons produced in collision of heavy ions are modified when propagating through the hot and dense medium of deconfined quarks and gluons known as the Quark Gluon Plasma. The study of jet substructure is an essential tool in quantifying this modification and in distinguishing between underlying mechanisms of parton-medium interactions. The latest CMS studies of jet substructure observables such as splitting function, groomed mass, boson-tagged fragmentation functions and jet shapes and their corresponding comparisons to theory give insight into the Quark Gluon Plasma, and how parton propagation is modified in QCD matter as opposed to matter.
The ability to differentiate between hadronically decaying massive particles and other sources of jets is increasingly important to the LHC physics program. A variety of algorithms which are used in ATLAS to identify large-R jets from such decays are presented, including both cut-based taggers and machine learning discriminants. In order to understand the validity of these identification algorithms, expectations are compared with data in well-understood final states, allowing for the derivation of scale factors accounting for differences between simulation and data.
Recent advances in neural networks and harsh pileup conditions in the second half on LHC Run 2 with on average 38 PU interactions, have sparked significant developments in techniques for jet tagging. Through the study of jet substructure properties, jets originating from quarks, gluons, W/ Z/Higgs bosons, top quarks and pileup interactions are distinguished, surpassing previous performance at lower pileup conditions by using new approaches. This talk will give an overview of the development of machine learning based jet substructure algorithms and their validation using the data collected by the CMS Experiment.
Many extensions to the Standard Model predicts new particles decaying into two bosons (W, Z, photon, or Higgs bosons) making these important signatures in the search for new physics. Searches for such diboson resonances have been performed in final states with different numbers of leptons, photons and jets and b-jets where new jet substructure techniques to disentangle the hadronic decay products in highly boosted configuration are being used. This talk summarizes recent ATLAS searches with LHC Run 2 data collected.
When are two collider events similar? In this talk, I answer this question by developing a metric between the events based on the earth mover's distance: the “work” required to rearrange one event into the other. With a metric in hand, I will focus on exploring the metric space of jets. Our metric allows us to visualize the space of jets, extract their dimensionality, perform jet classification, make contact with existing observables, identify the most and least typical jet configurations, and quantify the impact of detector effects in new ways.
https://www.cambridgebrewingcompany.com/
Jet reconstruction analyses at the high-luminosity phase of the LHC will face a similar challenge as current heavy-ion studies: how to mitigate the impact of the overwhelming and fluctuating energy coming from unrelated soft interactions (pileup/underlying event) on physical observables. In order to address this pressing issue, we propose to improve the momentum reconstruction resolution by exploiting intrinsic correlations among the soft and hard sectors of QCD jets. Our data-driven approach [1] results into a 5-40% improvement on the resolution of the reconstructed jet $p_T$ compared to previous methods in a high-luminosity proton-proton scenario. Its applicability in a heavy-ion context will be also discussed.
[1] Yacine Mehtar-Tani, Alba Soto-Ontoso, Marta Verweij, arXiv:1904.12815
We study the local properties of hadronic event activities using leptonic decaying Z bosons. We use the dimuon events in 8 TeV pp collisions from CMS open data, and we define the "leptonic Z jet" by enclosing particles within an angle R from the Z or by using standard jet clustering algorithms. A new hadronic observable called Z drop is defined which allows us to probe underlying events and pileup contributions. We examine the dependence on the Z transverse momentum, the radius R and the number of pileup events in real data and simulations, and we test the performance of pileup mitigation methods on this observable. The measurement will provide useful information about soft particle distribution uncorrelated with the leptonic Z decay, which allows us to perform precision studies of hadronic decays of Z bosons in future work. We also propose to use Z drop as a new probe of the quark-gluon plasma produced in heavy ion collisions.
Measurements of jets produced in collisions of heavy ions, such as dijet asymmetry, boson-jet momentum imbalance, and inclusive jet spectra, have consistently indicated final states of less energy as compared to vacuum reference. This energy loss is interpreted as signature of Quark-Gluon Plasma, the hot and dense medium of deconfined partons produced in the collision of relativistic nuclei. Subsequent studies have shown that the energy lost by jets is redistributed to large angle and in multiplicities of softer particles. In this talk, a thorough accounting of jet energy redistribution through missing momentum techniques, radial scans of jet spectra, and large angle jet shapes as measured with the CMS detector. These results can distinguish between mechanisms of parton-medium interaction as encoded in various Monte Carlo and give insight into the medium response.
Jets are now routinely used to probe the quark-gluon plasma (QGP) created in high-energy heavy-ion collisions at the LHC. This talk is meant to report on recent work towards developing a complete picture of how parton cascades and jets form in the QGP in QCD, including both standard parton shower and medium-induced emissions. The talk will first introduce a picture valid in the leading logarithmic accuracy and then discuss its phenomenological implications, focusing mainly on the zg observable.
Jets are the experimental signatures of energetic quarks and gluons produced in high energy processes and they need to be calibrated in order to have the correct energy scale. A detailed understanding of both the energy scale and the transverse momentum resolution of jets at the CMS is of crucial importance for many physics analyses. Furthermore, study of jet substructure properties in boosted topologies are critical for distinguishing jets originating from quarks, gluons, W/ Z/Higgs bosons, top quarks and pileup interactions. Lastly, the precise measurement of the missing transverse momentum (MET) observable is critical for standard model measurements involving W, Z, and the Higgs bosons, and top quarks. MET is also one of the most important kinematic observable used in searches for physics beyond the standard model targeting new weakly interacting neutral particles.
In this talk, we present the measurements of CMS jet energy scale and resolution, MET performance and standard heavy object tagging performance using the data sample collected in proton-proton collisions at a center-of-mass energy of 13 TeV.
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point cloud, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph CNN for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and improves significantly over existing methods.
Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of interpretability. Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of an $S_{2} (R)$ deposit at an angular scale $R$ in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of the architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.
Despite the successful application of deep learning to many problems involving jet substructure, typical approaches involve representing jets either as lists of four-vectors or as 2D images. This is mainly due to the compatibility of these structures with existing architectures, such as recurrent or convolutional networks. However, these networks fail to exhibit equivariance with respect to obvious symmetries associated to jet physics, such as rotations and boosts. Recent work in the field of representation learning has shown that equivariant (i.e. symmetry-respecting) architectures generally improve learning, allowing networks to perform better with fewer parameters. Using the example of boson tagging, we demonstrate the importance of equivariance, particularly with respect to boosts, for jet observables. We propose representing jets as functions on the 2-sphere, and construct learnable feature-matching kernels using spherical harmonics. We then demonstrate a network whose layers compute generalized convolutional operations over the desired symmetry groups, automatically resulting in equivariant representations throughout the network.
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. We find that they are extremely powerful and great fun.
We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown underlying short distance physical processes in multi-jet events. In particular, we use a mixed membership model known as Latent Dirichlet Allocation to build a data-driven unsupervised top-quark tagger and ttbar event classifier. We compare our proposal to existing traditional and machine learning approaches to top jet tagging. Finally, employing a toy vector-scalar boson model as a benchmark, we demonstrate the potential for discovering New Physics signatures in multi-jet events in a model independent and unsupervised way.
We introduce a novel implementation of a reinforcement learning algorithm which is adapted to the problem of jet grooming, a crucial component of jet physics at hadron colliders. We show that the grooming policies trained using a Deep Q-Network model outperform state-of-the-art tools used at the LHC such as Recursive Soft Drop, allowing for improved resolution of the mass of boosted objects. The algorithm learns how to optimally remove soft wide-angle radiation, allowing for a modular jet grooming tool that can be applied in a wide range of contexts.
Machine learning methods are being increasingly and successfully applied to many different physics problems. However, current machine learning approaches do not model uncertainties well - if at all. In this talk I will discuss how using Bayesian neural networks can give us a handle on uncertainties in machine learning. I will use tagging top quark vs. light quark and gluon jets as an example of how these networks are competitive with other neural network taggers with the advantage of providing an event-by-event uncertainty on the classification. I will then further discuss how this uncertainty changes with experimental systematic effects, using pile-up and jet energy scale as examples.
Parton shower Monte Carlo programs are a key tool for all aspects of analysis using jet substructure. These programs have many tunable parameters that control aspects of both perturbative and non-perturbative models. Finding the best parameters is non-trivial, and parton showers are typically run both for some optimized parameters as well as variations for uncertainty estimates.
Traditionally, tuned parameters are found using a set of one-dimensional unfolded measurements and optimized using various approximate sampling methods. Simulations with new parameters can be costly and must be run for every new set of parameter values, except for some limited cases where analytic weights can be calculated.
We propose a new data-driven method which uses deep learning with jet constituents to calculate the weights relating any point in the parameter space to another. We show how this method can be trained to relate two discrete points or to interpolate continuously in parameter space. In the continuous case, it can be used to fit for the optimal Monte Carlo parameters by using gradient descent in a classification task between a MC sample with known parameter values and the “data”.
We present an innovative end-to-end deep learning approach for jet identification at the LHC. The method combines deep neural networks with low-level detector information, such as calorimeter energy deposits and tracking information, to build a discriminator to identify different particles. Using two physics examples as references: electron and photon discrimination and quark and gluon discrimination, we demonstrate the performance of the end-to-end approach using simulated events with full detector geometry available as CMS Open Data. We also offer insights into the importance of the information extracted from various sub-detectors and describe how end-to-end techniques can be extended to event-level classification using information from the whole detector.
Monte Carlo event generators remain an indispensable tool in the reconstruction of boosted objects. Typically, in parton shower Monte Carlos, coloured resonances radiate only in production, while any coloured decay products radiate independently of this. This approach fails to take into account interference between the radiation produced in production and decay. Inclusion of these coherence effects not only modifies the radiation pattern, but a different recoil strategy must be employed. Both of these features can potentially modify the shape of distributions used in the reconstruction of such resonances.
In this talk, we present a new implementation of coherent radiation from coloured resonances for VINCIA, an antenna-shower plug-in to the PYTHIA 8 Monte Carlo event generator. We consider top quark pair-production at the LHC as a case study, and present the impact on observables relevant for the measurement of the top quark mass.
In this talk I provide a field theory based description of hadronization power corrections for soft drop groomed measurements such as the jet mass. It is proven that the leading power corrections are described by 3 universal hadronic parameters, which are independent of the jet kinematics, jet radius, and soft drop grooming parameters zcut and beta. These corrections come with 2 non-trivial perturbatively calculable Wilson coefficients which modify the shape of the jet mass spectrum. Unlike other known examples, these hadronization corrections are not simply described by a standard shape function, nor by a shift and normalization correction. These predictions are compared to results from 3 MCs, Pythia, Herwig, and Vincia. The description of these power corrections is important for precision determinations of standard model parameters like the strong coupling and top-mass.
We study quark versus gluon discrimination systematically and present explicit calculations for jets on which up through three emissions are resolved. These explicit calculations enable determination of quantities central to machine learning, such as the likelihood, reducibility factors, and area under the ROC curve (AUC), to be calculated within a concrete approximation scheme. We prove many results regarding quark versus gluon discrimination including the reducibility factor for gluon jets with any number of resolved emissions, robust bounds on the AUC, and that the optimal observable for quark versus gluon discrimination is IRC safe.
Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain unknown. One of these unknown parameters is the Higgs boson total width. Currently, the best known approach to measure the Higgs boson total width at the LHC is indirectly through Higgs interference of off-shell Z boson pair production. In this paper, we present a new approach to constraining the Higgs total width by requiring the Higgs to be resolved as a single high pT jet. We show that this approach is capable of yielding similar sensitivity to the off-shell measurement. Additionally, we outline the theoretical limitations of this and present an attempt at utilizing machine learning to minimize the theoretical assumptions. Finally, we outline the required insights needed to make this approach a truly model independent measurement of the Higgs boson total width.
Multiparticle correlators are a broad class of observables that have found significant use at colliders. It is known that there exist mysterious linear relations between specific types of these correlators when their summands satisfy certain properties. In this talk, I will develop graphical methods to understand and classify all such linear relations, showing that they can be derived from a master antisymmetrization identity. Interesting connections to counting the number of independent polynomials in pairwise kinematic variables will be presented along with other potential applications.
The production of jets initiated by heavy flavour quarks (b-quarks and c-quarks) is important in many contexts, especially including studies of particles which couple more strongly to massive particles, such as the Higgs boson. The distinct properties of these b-jets and c-jets can be identified by the ATLAS detector and differentiated with respect to jets initiated by light quarks and gluons, where the algorithms used for tagging such heavy flavour jets will be discussed. Additionally, the identification of boosted decays of massive particles to pairs of b-quarks reconstructed within a single large-R jet will be presented, together with methods to evaluate the the differences between simulation and data.
We present new results from searches for beyond-the-standard model physics with highly boosted final states, where the use of jet substructure is essential for the identification of a potential signal. The searches cover uncommon jet substructure, such as jets containing a hard photon and hadronic activity from N-prong decays, or highly-boosted light resonances decaying to quark anti-quark pairs. Special emphasis is given to the identification of these signal jets and on the methods to derive the standard model backgrounds.
The high energy of the LHC allows access to large numbers of high transverse momentum top quarks. Measurements of differential cross-sections in top quark pair production at 13 TeV with the ATLAS detector are presented. They are performed using the lepton+jets and all-hadronic final states. Jet substructure techniques are used to identify hadronically decaying top quarks. The measurements are corrected for detector effects to obtain differential cross-sections at particle-level in a fiducial region close to the event selection. These measurements probe our understanding of top quark pair production in the TeV regime. The results, unfolded to particle and parton level, are compared to predictions of Monte Carlo generators implementing NLO matrix elements matched with parton showers and NNLO QCD theory calculations.
This talk will present recent advances in measurements of jet mass and jet substructure observables, providing important tests of QCD. The interplay of MC event generator tuning and jet substructure is also discussed.
We present precision measurements of Z𝛾 and Z+jet production utilising jet substructure techniques. They are performed at √s=13 TeV using the ATLAS detector. In the first measurement, the Z boson is reconstructed in the Z→b bbar decay channel, with both b-quarks contained within a large-radius high-transverse-momentum jet that is subsequently groomed to remove contributions from underlying events and additional proton-proton collisions. The Z→b bbar decay is identified using b-tagged track-jets. The measurement is performed twice using two grooming techniques, trimming and soft-drop. The fiducial cross-sections are measured and differential cross-sections for the b bbar invariant mass are presented. In addition, if available, a measurement of kinematic variables in events with a leptonically-decaying Z-boson and a large-radius high-transverse momentum trimmed jet are presented. Differential cross sections are measured in two phase space regions defined by the large R-jet having zero or two b-tagged track-jets.
The jet shape is the fraction of the jet transverse momentum within a cone $r$ centered on the jet axis. I will present a calculation of the jet shape at next-to-leading logarithmic accuracy plus next-to-leading order (NLL$'$), accounting for logarithms of both the jet radius $R$ and the ratio $r/R$. This is the first phenomenological study that takes the recoil of the jet axis due to soft radiation into account, which is needed to reach this accuracy. This recoil complicates the calculation of collinear radiation and requires the treatment of rapidity logarithms and non-global logarithms. I will present numerical results, finding good agreement with ATLAS and CMS measurements of the jet shape in an inclusive jet sample, pp$\to$jet+X, for different kinematic bins. The effect of the underlying event and hadronization are included using a simple one-parameter model, since they are not part of our perturbative calculation.
Over the years many different types of fits for the strong coupling
constant have been performed. However one type of high precision result
that currently significantly differs from the world average are results
from event shapes at electron positron colliders. One possible source
for the difference in these results could be the degeneracy between
the fit of the strong coupling constant and non-perturbative parameters.
In this talk I will explore the application of modern jet substructure
techniques, specifically soft drop, in order to break the impact of
the non-perturbative corrections on the fit of the strong coupling
constant.
Gluon splitting to b-quark pairs is a unique probe of the properties of gluon fragmentation, as the identified b-tagged jets provide a proxy for the quark daughters of the initial gluon. We present a measurement of key differential distributions related to g→b bbar using data collected with the ATLAS detector at √s=13 TeV. Track jets are used to probe angular scales below the standard R=0.4 jet radius. The observables are unfolded to particle level in order to facilitate direct comparison with predictions from simulations and provide an important constraint to hadronization models. A measurement of the properties of jet fragmentation performed with proton-proton collision data collected with the ATLAS detector at √s=13 TeV will also be presented. Charged particle tracks are used to measure charged particle multiplicity, the jet charge, the summed fragmentation function, the momentum transverse to the jet axis, and the radial profile of the jet. Each observable is unfolded to correct for acceptance and detector effects. Exclusive interpretations in terms of quarks and gluons are provided in order to directly compare with state-of-the-art calculations.
Particles produced in high energy collisions that are charged under one of the fundamental forces will radiate proportionally to their charge, such as photon radiation from electrons in quantum electrodynamics. At sufficiently high energies, this radiation pattern is enhanced collinear to the initiating particle, resulting in a complex, many-body quantum system. Classical Markov Chain Monte Carlo simulation approaches work well to capture many of the salient features of the shower of radiation, but cannot capture all quantum effects. We show how quantum algorithms are well-suited for describing the quantum properties of final state radiation. In particular, we develop a polynomial time quantum final state shower that accurately models the effects of intermediate spin states similar to those present in high energy electroweak showers. The algorithm is explicitly demonstrated for a simplified quantum field theory on a quantum computer. See 1904.03196 for details.
The energy-energy-correlator (EEC) observable measures the energy deposited in two detectors as a function of the angle between the detectors. The collinear limit, where the angle between the two detectors approaches zero, is of particular interest for describing the substructure of jets produced at hadron colliders as well as in $e^+e^-$ annihilation. We derive a factorization formula for the leading power asymptotic behavior in the collinear limit of a generic quantum field theory. The relevant anomalous dimensions are expressed in terms of the timelike data of the theory, in particular the moments of the timelike splitting functions, which are known to high perturbative orders. In QCD and in $\mathcal{N}=1$ super-Yang-Mills theory, we then perform the resummation to next-to-next-to-leading logarithm, improving previous calculations by two perturbative orders. In conformally invariant $\mathcal{N}=4$ super-Yang-Mills theory, a particular reciprocity between timelike and spacelike evolution can be used to express our factorization formula as a power law with exponent equal to the spacelike twist-two spin-three anomalous dimensions. This provides a connection between the timelike dynamics of jets and the spectrum of anomalous dimensions of local operators, which is amenable to techniques such as integrability, and we discuss implications of these relations away from the conformal limit.
Many new-physics signatures at the LHC produce highly boosted particles, leading to close-by objects in the detector and necessitating jet substructure techniques to disentangle the hadronic decay products. This talk presents the latest ATLAS results for searches for such resonances, including the Higgs boson or top-quark pairs, using 13 TeV data. It will explain the techniques used, including new top-tagging techniques using machine learning and the use of large-radius jets containing electrons.
We present new results from searches for beyond-the-standard model physics with highly boosted top quarks in the final state, where the reconstruction and identification of fully-merged hadronic top quark decays is an essential tool. The talk summaries the use of large-radius jets and substructure techniques in proton-proton collisions at 13 TeV. The searches cover a variety of models, such as heavy resonances decaying to top quarks, pair and single production of vector-like quarks, and the production of third generation squarks.
In this talk, I investigate jet substructure at the Large Hadron Collider with the CMS Open Data. I analyze a sample of jets from 2.3/fb of 7 TeV proton-proton collisions detected by the CMS experiment in 2011 with the companion simulated (both pre- and post-detector) datasets, focusing on a high-quality sample of jets with transverse momenta restricted to between 375 and 425 GeV. I further move to a specific analysis of jet classification using the unsupervised algorithm of jet topics to provide a new way of defining the categories of quark and gluon jets through their observable properties.
We introduce collinear drop jet substructure observables, which are unaffected by contributions from collinear radiation, and systematically probe soft radiation within jets. These observables can be designed to be either sensitive or insensitive to process-dependent soft radiation originating from outside the jet. Such collinear drop observables can be exploited as variables to distinguish quark, gluon, and color neutral initiated jets, for testing predictions for perturbative soft radiation in Monte Carlo simulations, for assessing methods of determining hadronization corrections, for examining the efficiency of pileup subtraction methods, and for any other application that leaves an imprint on soft radiation. We discuss examples of collinear drop observables that are based both on clustering and on jet shapes. Using the soft-collinear effective theory we derive factorization expressions for collinear drop observables from QCD jets, and carry out a resummation of logarithmically enhanced contributions at next-to-leading-logarithmic order. We also identify an infinite class of collinear drop observables for which the leading double logarithms are absent.
Many supersymmetric scenarios feature final states with non-standard final state objects. The production of massive sparticles can lead to the production of boosted top quarks or vector bosons, high-pT b-jets. The strongest limits from ATLAS on chargino-neutralino production come from an all-hadronic search for electroweak supersymmetry, one of the first of its kind. At the same time, transitions between nearly mass-degenerate sparticles can challenge the standard reconstruction because of the presence of very soft leptons or jets. The talk will review the application of innovative jet and MET reconstruction techniques to supersymmetry searches in ATLAS.
We present new results from searches for beyond-the-standard model physics with highly boosted Higgs and vector bosons in the final state. The talk summarizes the use of large-radius jets and substructure techniques used for the reconstruction and identification of fully-merged hadronic decays of these particles. New techniques to estimate the standard model backgrounds are discussed. The searches cover a variety of models, such as two-Higgs-doublet models or generic heavy resonances decaying to bosons.
Panel Members are Florencia Cannelli, Laeticia Mendez, Mihoko Nojiri Simone Marzani, Nhan Tran, David Miller
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