Deep-learning top taggers or the end of QCD?

Kasieczka, G.; Plehn, Tilman; Russell, Michael; Schell, Torben

doi:10.1007/jhep05(2017)006

Cited by 217 publications

(285 citation statements)

References 76 publications

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“…While the benchmark points representing a cluster are isolated points in the parameter space, the procedure we propose here allows us to associate certain shapes more straightforwardly with distinct regions in the parameter space. The application of machine learning techniques in high energy physics, in particular to constrain the EFT/new physics parameter space, has been brought forward already some time ago [68][69][70][71], with successful applications in jet and top quark identification [72][73][74][75][76][77][78][79][80][81], new physics searches [70,71,[82][83][84][85][86][87][88][89][90] and PDFs [91]. Shape analysis with machine learning has been applied already to constrain anomalous Higgs-vector boson couplings in HZ production [92].…”

Section: Introductionmentioning

confidence: 99%

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Capozi

Heinrich

2020

J. High Energ. Phys.

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We classify shapes of Higgs boson pair invariant mass distributions m hh , calculated at NLO with full top quark mass dependence, and visualise how distinct classes of shapes relate to the underlying coupling parameter space. Our study is based on a five-dimensional parameter space relevant for Higgs boson pair production in a non-linear Effective Field Theory framework. We use two approaches: an analysis based on predefined shape types and a classification into shape clusters based on unsupervised learning. We find that our method based on unsupervised learning is able to capture shape features very well and therefore allows a more detailed study of the impact of anomalous couplings on the m hh shape compared to more conventional approaches to a shape analysis.

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Section: Introductionmentioning

confidence: 99%

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Capozi

Heinrich

2020

J. High Energ. Phys.

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“…We use the "uniform phase space" scheme to flatten discriminating variables, which was introduced in [6] to quantify the information learned by deep neural networks. For other suggestions on testing what the machines are learning, see [7][8][9][10][11][12]. A nice summary of these ideas can be found in [13].…”

Section: Introductionmentioning

confidence: 99%

What is the machine learning?

2018

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Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables-aided by physical intuition-that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

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“…The N -subjettiness ratio, in particular, τ 21 is especially suitable to find these objects. Since t(e) jets are generically characterized by 2 subjets, one finds τ 2 τ 1 , or in other words, τ 21 1 as can be seen in the middle right panel of figure 2. For all other jets we tend to get comparatively larger values of τ 21 .…”

Section: Variables In V Ementioning

confidence: 85%

“…Tagging these top jets, which contains all the decay products of hadronically decaying top quarks is quite a mature field. A plethora of tagging algorithms have been proposed which range from the substructure analyses [2][3][4][5][6][7][8][9][10][11][12][13] to methods taking full advantage of recent advances in the machine learning [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Jets with electrons from boosted top quarks

Chatterjee

Godbole

Roy

2020

J. High Energ. Phys.

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We propose a method to identify jets consisting of all the visible remnants of boosted top particles when these decay semileptonically to electrons. Within these jets, the electron shower overlaps with the shower initiated by the b quark, which makes the identification of the electron hard. Even if an electron inside a jet is identified, it is difficult to pinpoint whether the electron rich jet is indeed due to top quark decay or not, since the invisible neutrino carries away a nontrivial part of the energy-momentum of the original top quark. Broadly speaking, the method proposed here has three key components. It uses the distribution of energy in various parts of the detector to identify whether the observed jet is consistent with a jet containing an energetic electron. It uses the substructure of the jet to determine the momentum associated with the electron. Finally, it constructs new variables that carry tell-tale features of top quark decay kinematics using an extra ansatz that, there exists a massless invisible four-momentum roughly collimated to the electron, which reconstructs a W and a top when it is combined with the electron and the full jet respectively. We demonstrate the efficacy of this proposal using simulated data and show that our method not only reduces the backgrounds from light flavor jets, b jets from QCD, and hadronic top jets, it can also tell apart jets rich in electrons but not due to top quark decays.

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Deep-learning top taggers or the end of QCD?

Cited by 217 publications

References 76 publications

Exploring anomalous couplings in Higgs boson pair production through shape analysis

Exploring anomalous couplings in Higgs boson pair production through shape analysis

What is the machine learning?

Jets with electrons from boosted top quarks

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