16–21 Jul 2017
Embassy Suites Buffalo
US/Eastern timezone

Contribution List

72 out of 72 displayed
Export to PDF
  1. Andrew Larkoski (Reed Collge)
    17/07/2017, 09:00
  2. Lily Asquith (University of Sussex (UK))
    17/07/2017, 09:45
  3. Bogdan Dobrescu (Fermilab)
    17/07/2017, 11:00
  4. Jonathan Burr (University of Oxford (GB))
    17/07/2017, 11:30

    Models predicting the production and decay of supersymmetric (SUSY) particles often have promising search channels involving decays through heavy intermediate states such as top quarks and heavy bosons. However, unlike in most exotics scenarios these heavy states are only moderately boosted which can make traditional substructure techniques less useful and motivates the development of...

    Go to contribution page
  5. Jennifer Ngadiuba (CERN), Jennifer Ngadiuba (Univ. Zürich)
    17/07/2017, 12:00

    Beyond the standard model theories like Extra-Dimensions and Composite Higgs scenarios predict the existence of very heavy resonances compatible with a spin 0 (Radion),spin 1 (W’, Z’) and spin 2 (Graviton) particle with large branching fractions in pairs of standard model bosons and negligible branching fractions to light fermions. We present an overview of searches for new physics containing...

    Go to contribution page
  6. Daniel Isaac Narrias Villar (Ruprecht-Karls-Universitaet Heidelberg (DE))
    17/07/2017, 14:00

    Several theories beyond the standard model predict new particles decaying resonantly into dibosons or coupling to dark matter particles. Jet substructure and boson tagging techniques play a crucial role in searches for dark matter and diboson resonances in boosted topologies. In this talk, the application of these techniques at ATLAS will be discussed in the context of recent searches for dark...

    Go to contribution page
  7. Thomas Peiffer (Hamburg University (DE)), Thomas Peiffer (University of Hamburg)
    17/07/2017, 14:30

    We present a search for new massive particles (such as Z prime and W prime resonances) decaying to heavy-flavour quarks with the CMS detector at the LHC, and dark matter signatures involving boosted jets. Resonant ttbar, tb, and heavy quark plus vector-like quark production, along with missing pt plus boosted objects, are investigated. We use proton-proton collision data recorded at a...

    Go to contribution page
  8. Junpei Maeda (Kobe University (JP))
    17/07/2017, 15:00

    Several models of physics beyond the Standard Model contain preferential couplings to top quarks. We present an overview of searches for new physics containing boosted top quarks in the final state, using proton-proton collision data collected with the ATLAS detector at the LHC at a centre-of-mass energy of 13 TeV. These results cover heavy gauge bosons, excited third generation quarks, or...

    Go to contribution page
  9. Daniel Gonzalez Vazquez (Hamburg University (DE))
    17/07/2017, 15:30

    We present results of searches for massive top and bottom quark partners using proton-proton collision data collected with the CMS detector at the CERN LHC at a center-of-mass energy of 8 and 13 TeV. These considered models include vector-like quarks, excited quarks and supersymmetric quark partners. These particles can be produced singly or in pair and their decays result in a variety of...

    Go to contribution page
  10. Benjamin Elder (Massachusetts Institute of Technology)
    17/07/2017, 16:30

    I will discuss fractal jet observables, which are collinear-unsafe but can be described by generalizing the formalism of fragmentation functions. Generalized fragmentation functions (GFFs) are nonperturbative objects with a calculable RG running. In contrast to the linear DGLAP equations for ordinary fragmentation functions, GFFs evolve nonlinearly, since they encode correlations among subsets...

    Go to contribution page
  11. Frederic Alexandre Dreyer (MIT)
    17/07/2017, 17:00

    In this talk, we introduce a new jet substructure method based on a
    recursive iteration of the Soft Drop algorithm through both branches
    of the clustering tree.
    Recursive soft drop uses an additional parameter N to define the
    number of layers of soft drop declustering, providing an optimized
    grooming strategy for boosted objects with (N+1)-prong decays, as well
    as improved stability in high...

    Go to contribution page
  12. Aashish Tripathee (Massachusetts Institute of Technology)
    17/07/2017, 17:30

    In this talk, I present the first analysis of the substructure of jets using the 2010 CMS Open Data. Our analysis is based on 36/pb of 7 TeV proton-proton collisions, where in each event the leading jet has a transverse momentum larger than 150 GeV. We measure classic jet substructure observables like jet mass and multiplicity and compare the results to parton shower generators. We find...

    Go to contribution page
  13. Dr Yang-Ting Chien (Massachusetts Institute of Technology)
    17/07/2017, 18:00

    We present the first calculations of the momentum sharing and angular separation distributions between the leading subjets inside a reconstructed jet, as well as the jet mass distribution modification in heavy ion collisions. These observables are sensitive to the early and late stages of the in-medium parton shower evolution and allow us to probe the quark-gluon plasma across a wide range of...

    Go to contribution page
  14. Konrad Tywoniuk (CERN)
    17/07/2017, 18:30

    Significant experimental and theoretical activity at the LHC is dedicated to the study of hot and dense QCD matter created in head-on heavy-ion collisions. Measurements of fully reconstructed jets in these collisions allow to examine new aspects of this exotic state via its coupling to perturbative degrees of freedom. The potential sensitivity of jet substructure observables to modifications...

    Go to contribution page
  15. John Campbell
    18/07/2017, 08:30
  16. Doreen Wackeroth (SUNY Buffalo)
    18/07/2017, 09:00
  17. Junmou Chen (University of Pittsburgh)
    18/07/2017, 09:30

    We derive the electroweak (EW) collinear splitting functions up to single logs. Especially we systematically incorporate EW symmetry breaking (EWSB), by imposing a particularly convenient gauge choice (dubbed “Goldstone Equivalence Gauge”) that disentangles the effects of Goldstone bosons and gauge fields in the presence of EWSB. As a result, we are able to derive splitting functions up to...

    Go to contribution page
  18. Mr Aditya Pathak (Massachusetts Institute of Technology)
    18/07/2017, 10:00

    We show how the top mass can be extracted kinematically using cross sections for event shapes observables calculated using effective field theory methods. With the help of Soft Drop grooming done at a level that does not disturb the radiation that can modify the top mass definition, while still isolating the top jet, we obtain a distribution that is only mildly sensitive to the underlying...

    Go to contribution page
  19. Marek Schoenherr (Universitaet Zuerich (CH))
    18/07/2017, 11:00
  20. Stefan Prestel
    18/07/2017, 11:30
  21. Michela Paganini (Yale University (US)), Luke Percival De Oliveira, Ben Nachman (Lawrence Berkeley National Lab. (US))
    18/07/2017, 12:00

    We introduce the first use of deep neural network-based generative modeling for high energy physics (HEP). Our novel Generative Adversarial Network (GAN) architecture is able cope with the key challenges in HEP images, including sparsity and a large dynamic range. For example, our Location-Aware Generative Adversarial Network learns to produce realistic radiation patterns inside high energy...

    Go to contribution page
  22. Laís Sarem Schunk (IPhT, CEA - Saclay)
    18/07/2017, 14:00

    We perform a phenomenological study of the invariant mass distribution of hadronic jets produced in pp collisions, in conjunction with a groomer, in particular the modified MassDrop Tagger (equivalent to Soft Drop with angular exponent $\beta = 0$). Our calculation resums large logarithms of the jet mass and includes the full dependence on the groomer’s energy threshold $z_\text{cut}$ , and it...

    Go to contribution page
  23. Giulia Ucchielli (Universita e INFN, Bologna (IT))
    18/07/2017, 14:30

    Boosted topologies allow to explore Standard Model processes in kinematical regimes never tested before. In such LHC challenging environments, standard reconstruction techniques quickly hit the wall. Targeting hadronic final states means to properly reconstruct energy and multiplicity of the jets in the event. In order to be able to identify the decay product of boosted objects, i.e. W bosons,...

    Go to contribution page
  24. Philip Coleman Harris (CERN)
    18/07/2017, 15:00

    A number of measurements are presented that utilize and/or investigate jet substructure.

    The measurement of top production and the investigation of its properties in the boosted regime is gaining increasing attention with the rapid increase of the production cross sections at 13 TeV. The CMS experiment has measured the production cross section as function of the transverse momentum and...

    Go to contribution page
  25. Christopher Frye (Harvard)
    18/07/2017, 16:00

    Charged track multiplicity is among the most powerful observables for discriminating quark- from gluon-initiated jets. Despite its utility, it is not infrared and collinear (IRC) safe, so perturbative calculations are limited to studying the energy evolution of multiplicity moments. While IRC-safe observables, like jet mass, are perturbatively calculable, their distributions often exhibit...

    Go to contribution page
  26. Patrick Komiske (MIT)
    18/07/2017, 16:30

    I will discuss recent work addressing light-quark/gluon jet discrimination using image recognition techniques from deep learning. The usual jet-image framework is supplemented by adding “color" to the images in the form of local energy deposit and count information. Overall, this approach outperforms multivariate analyses of traditional jet observables and provides a theoretical upper bound of...

    Go to contribution page
  27. Francesco Rubbo (SLAC National Accelerator Laboratory (US))
    18/07/2017, 17:00

    Distinguishing quark-initiated from gluon-initiated jets is useful for many measurements and searches at the LHC. We present a quark-initiated versus gluon-initiated jet tagger from the ATLAS experiment using the number of reconstructed charged particles inside the jet. The measurement of the charged-particle multiplicity inside jets from Run 1 is used to derive uncertainties on the tagger...

    Go to contribution page
  28. Yuta Takahashi (Univ. Zürich), Yuta Takahashi (Universitaet Zuerich (CH))
    18/07/2017, 17:30

    Distinguishing between quark and gluon initiated jets relies on differences in the QCD shower patterns and is an important ingredient for a number of physics analyses. We present the current status of quark gluon tagging in CMS including comparisons using 13 TeV collision data.

    Go to contribution page
  29. Xing Wang (University of Pittsburgh)
    19/07/2017, 08:30

    We present a phenomenological study of the Higgs radiative decay to a fermion pair. We include the chirality-flipping diagrams via the Yukawa couplings at the order $\mathcal{O}(y_f^2 \alpha)$, the chirality-conserving contributions via the top-quark loops of the order $\mathcal{O}(y_t^2 \alpha^3)$, and the electroweak loops at the order $\mathcal{O}(\alpha^4)$. All the leptonic radiative...

    Go to contribution page
  30. Zhuoni Qian (University of Pittsburgh)
    19/07/2017, 08:45

    We study the Higgs boson (h) decay to two light jets at the 14 TeV High-Luminosity-LHC (HL-LHC), where a light jet (j) represents any non-flavor tagged jet from the observational point of view. The decay mode h→gg is chosen as the benchmark since it is the dominant channel in the Standard Model (SM), but the bound obtained is also applicable to the light quarks (j=u,d,s). We estimate the...

    Go to contribution page
  31. Peter Loch (University of Arizona (US))
    19/07/2017, 09:00

    Small radius jets with R = 0.4 are standard tools in ATLAS for physics analysis. They are calibrated using a sequence of Monte Carlo simulation-derived calibrations and corrections followed by in-situ calibrations based on the transverse momentum balance between the probed jets and well-measured reference signals. In this talk the inputs to jet reconstruction in LHC Run 2 comprising...

    Go to contribution page
  32. Anastasia Karavdina (Hamburg University (DE)), Anastasia Karavdina (University of Hamburg)
    19/07/2017, 09:30

    Measurement of jet energy scale corrections and resolution, and performance of jet mass scale and resolution based on data collected at a center-of-mass energy of 13 TeV are presented in this report. Jet energy scale corrections at CMS accounts for the effects of pileup, and dependencies of response of jets on transverse momenta and detector non-uniformity. The differences in response measured...

    Go to contribution page
  33. Joe Taenzer (Tel Aviv University (IL))
    19/07/2017, 10:00

    Large-R jets are used by many ATLAS analyses working in boosted regimes. ATLAS Large-R jets are reconstructed from locally calibrated calorimeter topoclusters with the Anti-k_{t} algorithm with radius parameter R=1.0, and then groomed to remove pile-up with the trimming algorithm with f_{cut} 0.05 and subjet radius R=0.2. Monte Carlo based energy and mass calibrations correct the...

    Go to contribution page
  34. Thea Aarrestad (Universitaet Zuerich (CH)), Thea Aarrestad (Univ. Zürich)
    19/07/2017, 11:00

    The case of CMS "two prong" tagging algorithms are presented, specifically detailing the cases of W and H boson tagging. This talk will focus on the most recent algorithms used in LHC Run II analyses and their validation in data.

    Go to contribution page
  35. Nurfikri Norjoharuddeen (University of Oxford (GB))
    19/07/2017, 11:30

    We present updates of W, Top and Higgs tagging studies with the ATLAS detector. The performance of 2 variable taggers, HEPTopTagger and shower deconstruction are compared in Monte Carlo simulations. To asses the modelling of the taggers’ performance, the tagging efficiencies are measured, with the full 2015+2016 dataset, in semi-leptonic top quark pair events and the background rejections are...

    Go to contribution page
  36. Torben Dreyer (Hamburg University (DE)), Torben Dreyer (University of Hamburg)
    19/07/2017, 12:00

    An overview of methods for identifying decays of boosted top quarks with the CMS detector in Run II is presented.

    Go to contribution page
  37. Todd Brian Huffman (University of Oxford (GB))
    19/07/2017, 14:00

    The performance of standard tagging algorithms begins to fall in the case of highly boosted B hadrons (γβ=p/m>200). This work builds on our previous study that uses the jump in hit multiplicity among the pixel layers of an ATLAS or CMS-like detector when a B hadron decays within the detector volume. Consequently, tracking is not required.

    First, multiple pp interactions within a finite...

    Go to contribution page
  38. Gregor Kasieczka (Eidgenoessische Technische Hochschule Zuerich (CH))
    19/07/2017, 14:30

    Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat...

    Go to contribution page
  39. Oliver Majersky (Comenius University (SK))
    19/07/2017, 15:00

    We present techniques for the identification of hadronically-decaying W bosons and top quarks using high-level features as inputs to boosted decision trees and deep neural networks in the ATLAS experiment at sqrt(s)=13 TeV. The performance of these machine learning based taggers is compared in Monte Carlo simulation with various different tagging algorithms. An improvement in background...

    Go to contribution page
  40. Jan Kieseler (CERN), Jan Kieseler (CERN)
    19/07/2017, 15:30

    Machine learning has become an important tool in particle physics, and in jet substructure and boosted objects in particular. This presentation shows the breadth of applications from CMS, from "DeepFlavor" b-tagging to new techniques of substructure applications.

    Go to contribution page
  41. Eric Metodiev (Massachusetts Institute of Technology)
    19/07/2017, 16:00

    We develop a new pileup mitigation technique based on multi-channel jet images using convolutional neural nets. The input to the network is a three-channel jet image: the calorimeter "pixel" information of charged leading vertex particles, charged pileup particles, and neutral particles . We compare our algorithm to existing methods on a wide range of simple and complex jet observables up to...

    Go to contribution page
  42. Gregory Soyez (IPhT, CEA Saclay)
    19/07/2017, 16:30
  43. 19/07/2017, 16:50
  44. 19/07/2017, 17:10
  45. 19/07/2017, 17:30
  46. Jeffrey Forshaw (University of Manchester)
    19/07/2017, 19:00
  47. Jennifer Kathryn Roloff (Harvard University (US))
    20/07/2017, 09:00

    Pileup is one of the biggest challenges facing the LHC and HL-LHC physics programs. Many reconstruction methods have been proposed for mitigating its effects across a broad range of physics metrics such as jet and jet substructure response and resolution, missing transverse energy performance, and lepton identification. Among the most successful are the SoftKiller and Pileup Per Particle...

    Go to contribution page
  48. Jennifer Kathryn Roloff (Harvard University (US))
    20/07/2017, 09:30

    Simultaneous proton-proton collisions, or pileup, at the LHC has a significant impact on jet reconstruction, requiring the use of advanced pileup mitigation techniques. Pileup mitigation may occur at several stages of the reconstruction process, and ATLAS uses a combination of schemes, including constituent reconstruction methods, constituent-level pileup-mitigation techniques, and jet-level...

    Go to contribution page
  49. Leonora Vesterbacka (Eidgenoessische Technische Hochschule Zuerich (CH)), Minna Leonora Vesterbacka (ETH Zürich)
    20/07/2017, 10:00

    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.

    Go to contribution page
  50. Anna Kathryn Duncan (University of Glasgow (GB))
    20/07/2017, 11:00

    The High-Luminosity LHC aims to provide a total integrated luminosity of 3000/fb from proton-proton collisions at sqrt(s) = 14 TeV over the course of ~10 years, reaching instantaneous luminosities of up to L = 7.5 x 10^34/cm^2/s, corresponding to an average of 200 inelastic p-p collisions per bunch crossing (mu = 200). Fast simulation studies have been carried out to evaluate the prospects of...

    Go to contribution page
  51. Julie Hogan (Brown University (US))
    20/07/2017, 11:30

    The prospects for boosted physics and jet substructure at the HL-LHC are presented, along with technical capabilities and updates to the detector that will assist in these measurements and searches.

    Go to contribution page
  52. Lars Rickard Strom (CERN)
    20/07/2017, 12:00

    The Compact Linear Collider project envisages an electron-positron collider
    with a low-energy stage at sqrt(s) = 380 GeV and an ultimate center-of-mass
    reach up to 3 TeV. Detailed Monte Carlo simulation studies of the detector
    are performed to optimize the design of the experiment and to understand
    the physics potential. CLIC aims to meet the challenging requirements on
    jet reconstruction...

    Go to contribution page
  53. Sergei Chekanov (Argonne National Laboratory (US))
    20/07/2017, 12:30

    We discuss performance requirements for future detectors in the context of reconstruction of multi-TeV objects (single particles and jets) at a 100 TeV collider. A software framework based on a Geant4 simulation together with a realistic reconstruction of tracks and calorimeter clusters is presented. Using this framework, we discuss response and momentum resolution of the tracker and the...

    Go to contribution page
  54. Gregory Soyez (IPhT, CEA Saclay)
    21/07/2017, 09:00
  55. Christoph Falk Anders (Ruprecht-Karls-Universitaet Heidelberg)
    21/07/2017, 09:45
  56. Prof. Joel Walker (Sam Houston State University)

    We introduce a new scale-invariant jet clustering algorithm which does not impose a fixed cone size on the event. The proposed construction maintains excellent object discrimination for very collimated partonic systems. Nevertheless, it is able to asymptotically recover favorable behaviors of the standard anti-KT algorithm. Additionally, it is intrinsically suitable for the tagging of highly...

    Go to contribution page
  57. Taoli Cheng (University of Chinese Academy of Sciences)

    Since the machine learning techniques are improving rapidly, it has been shown that the image recognition technique can be used to detect jet substructure. And it turns out that deep neural networks can match or outperform traditional approach. To push it further, we investigate the Recursive Neural Networks (RecNN), which embeds jet clustering history recursively as in natural language...

    Go to contribution page
  58. Mr Kaustuv Datta (Reed College)

    Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics has typically employed image recognition, natural language processing, or other algorithms that have been extensively developed...

    Go to contribution page
  59. Tatsumi Nitta (Waseda University (JP))
  60. Samuel Ross Meehan (University of Washington (US))
  61. Steven Randolph Schramm (Universite de Geneve (CH))
  62. Charles Irving Harrington (State University of New York at buffalo (US))
  63. Anne Winifred Fortman (State University of New York at Buffalo (US))
  64. Matt Leblanc (TRIUMF)
  65. Bahareh Hojatollah Roozbahani (State University of New York at buffalo (US))
  66. Anders Andreassen (Harvard University)

    The measurement of the top quark mass has large systematic uncertainties coming from the Monte Carlo simulations that are used to match theory and experiment. We explore how much that uncertainty can be reduced by using jet grooming procedures. We estimate the inherent ambiguity in what is meant by Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty can be reduced...

    Go to contribution page
  67. Cristina Ana Mantilla Suarez (Johns Hopkins University (US))
  68. Alejandro Gomez Espinosa (Rutgers, State Univ. of New Jersey (US))
  69. In this project, we apply the QT resummation on Pull Vector, which is which is a tool applied on the QCD color connection and this tool can help us to separate the background and signal for the process of Higgs and gluon decay to b\bar{b}.

    And in the presentation, I will show some of our works like the result of resummation and try to compare it with the next-to-leading fix-order dipole...

    Go to contribution page
  70. Justin Pilot (University of California Davis (US))

    We present a novel approach to the problem of discriminating jets produced from the hadronic decays of highly-boosted heavy particles (top, W, Z, H) from light jets. By hypothesizing different particle origins for the jets and boosting all jet constituents into the corresponding rest frames, angular and kinematic distributions of reconstructed particles can be used to discriminate 2- or...

    Go to contribution page
  71. Ashley Marie Parker (State University of New York at buffalo (US))
  72. As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics...

    Go to contribution page