Systematics of quark/gluon tagging

Gras, P.; Höche, Stefan; Kar, D.; Larkoski, Andrew J.; Lönnblad, Leif; Plätzer, Simon; Siódmok, Andrzej; Skands, Peter; Soyez, Grégory; Thaler, Jesse

doi:10.1007/jhep07(2017)091

Cited by 130 publications

(174 citation statements)

References 131 publications

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“…While the CMS detector reconstructs fewer charged PFCs than expected from Pythia at the generation level, the difference is well within the theoretical uncertainties in MC generation (see further discussion in Ref. [132]). Since we will not attempt to unfold the data in this paper, it is important for us to use observables that are robust to detector effects.…”

Section: Jet Substructure Observablessupporting

confidence: 68%

Exploring the space of jets with CMS open data

et al. 2020

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We explore the metric space of jets using public collider data from the CMS experiment. Starting from 2.3 fb −1 of proton-proton collisions at √ s = 7 TeV collected at the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central jets with transverse momentum above 375 GeV. To validate the performance of the CMS detector in reconstructing the energy flow of jets, we compare the CMS Open Data to corresponding simulated data samples for a variety of jet kinematic and substructure observables. Even without detector unfolding, we find very good agreement for trackbased observables after using charged hadron subtraction to mitigate the impact of pileup. We perform a range of novel analyses, using the "energy mover's distance" (EMD) to measure the pairwise difference between jet energy flows. The EMD allows us to quantify the impact of detector effects, visualize the metric space of jets, extract correlation dimensions, and identify the most and least typical jet configurations. To facilitate future jet studies with CMS Open Data, we make our datasets and analysis code available, amounting to around two gigabytes of distilled data and one hundred gigabytes of simulation files. CONTENTS

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Section: Jet Substructure Observablessupporting

confidence: 68%

Exploring the space of jets with CMS open data

et al. 2020

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“…However, a fit was again possible, following similar methodology. Out of the observables measured, LHA [41,42], D 2 [43], and ECF2 norm [44] substructure variables using trimming [45] were used. These choices are somewhat arbitrary, but intended to cover substructure variables with different sensitivity.…”

Section: Atlas Run 2 Jet Substructurementioning

confidence: 99%

Fast simulation of detector effects in Rivet

2020

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We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS. MC truth Detector hits Digitization TriggerDet Reco

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“…At double logarithmic order, the hard, initiating parton defines the jet flavor and so there is no ambiguity in the definition of quark and gluon jets. The subtleties in defining a jet flavor beyond this accuracy have been addressed by the community in a review article [15] and it remains an active research direction, with recent efforts to define quark and gluon jets based directly upon mutual irreducibility ideas [16,17]. We also do not include non-perturbative physics due to hadronization, for example, which would be needed for precision comparison to data.…”

Section: Approximations and Observablesmentioning

confidence: 99%

“…16 through N = 4 for β = 1. For comparison, we also show the results using 15-body phase space, namely all N -subjettiness values with β ∈ {0.5, 1.0, 2.0} and N up to 15. This has been established to achieve competitive quark vs. gluon classification performance with other machine learning methods, and so provides us with a proxy for absolute convergence of the ROC curve.…”

Section: Probing Machine Learning Strategiesmentioning

confidence: 99%

“…Identifying light jets as arising from quarks or gluons is a fundamental challenge for collider physics at the Large Hadron Collider (LHC). Many efforts have proposed new observables [1][2][3][4][5][6][7][8][9][10][11][12] or jet flavor definitions [13][14][15][16][17], completed theoretical calculations [18][19][20][21], and used machine learning methods [22][23][24][25][26] to push the boundaries of the discrimination power between quark and gluon jets. While these studies have led to steady improvements over time, they have been done with no clear organizing principle or agreed-upon "best" discrimination strategy.…”

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confidence: 99%

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A theory of quark vs. gluon discrimination

Larkoski

Metodiev

2019

J. High Energ. Phys.

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Understanding jets initiated by quarks and gluons is of fundamental importance in collider physics. Efficient and robust techniques for quark versus gluon jet discrimination have consequences for new physics searches, precision α s studies, parton distribution function extractions, and many other applications. Numerous machine learning analyses have attacked the problem, demonstrating that good performance can be obtained but generally not providing an understanding for what properties of the jets are responsible for that separation power. In this paper, we provide an extensive and detailed analysis of quark versus gluon discrimination from first-principles theoretical calculations. Working in the strongly-ordered soft and collinear limits, we calculate probability distributions for fixed N -body kinematics within jets with up through three resolved emissions (O(α 3 s )). This enables explicit calculation of quantities central to machine learning such as the likelihood ratio, the area under the receiver operating characteristic curve, and reducibility factors within a well-defined approximation scheme. Further, we relate the existence of a consistent power counting procedure for discrimination to ideas for operational flavor definitions, and we use this relationship to construct a power counting for quark versus gluon discrimination as an expansion in e C F −C A 1, the exponential of the fundamental and adjoint Casimirs. Our calculations provide insight into the discrimination performance of particle multiplicity and show how observables sensitive to all emissions in a jet are optimal. We compare our predictions to the performance of individual observables and neural networks with parton shower event generators, validating that our predictions describe the features identified by machine learning.

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Systematics of quark/gluon tagging

Cited by 130 publications

References 131 publications

Exploring the space of jets with CMS open data

Exploring the space of jets with CMS open data

Fast simulation of detector effects in Rivet

A theory of quark vs. gluon discrimination

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