Safety of Quark/Gluon Jet Classification

Romero, A.; Whiteson, D.; Fenton, M. J.; Collado, Julian; Baldi, Pierre

doi:10.48550/arxiv.2103.09103

Cited by 17 publications

(18 citation statements)

References 41 publications

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“…Such truth-level flavour assignment could for example be used as well-defined input, e.g. for machine-learning based methods, or as benchmark for flavour-tagging algorithms [106][107][108][109][110][111][112].…”

Section: Selected Parton-level Resultsmentioning

confidence: 99%

Phenomenology of jet angularities at the LHC

Reichelt,

Caletti,

Fedkevych

et al. 2021

Preprint

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We compute resummed and matched predictions for jet angularities in hadronic dijet and Z+jet events with and without grooming the candidate jets using the SoftDrop technique. Our theoretical predictions also account for nonperturbative corrections from the underlying event and hadronisation through parton-to-hadron level transfer matrices extracted from dedicated Monte Carlo simulations with SHERPA. Thanks to this approach we can account for nonperturbative migration effects in both the angularities and the jet transverse momentum. We compare our predictions against recent measurements from the CMS experiment. This allows us to test the description of quark-and gluon-jet enriched phase-space regions separately. We supplement our study with SHERPA results based on the matching of NLO QCD matrix elements with the parton shower. Both theoretical predictions offer a good description of the data, within the experimental and theoretical uncertainties. The latter are however sizeable, motivating higher-accuracy calculations.

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Section: Selected Parton-level Resultsmentioning

confidence: 99%

Phenomenology of jet angularities at the LHC

Reichelt,

Caletti,

Fedkevych

et al. 2021

Preprint

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“…It has actually been known for a long time that IRC safe observables are good quark vs. gluon jet discriminants [46,47]. Further, it has been demonstrated from construction of a deep neural network that the likelihood for quark vs. gluon discrimination is consistent with IRC safety [48][49][50]. Again, I want to emphasize that we, humans, learned something about QCD by thinking like a machine.…”

Section: < L a T E X I T S H A 1 _ B A S E 6 4 = " H R S 0 D D P G X ...mentioning

confidence: 99%

Jet Physics from the Ground Up

Larkoski¹

2021

Preprint

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These are lecture notes presented at the online 2021 QUC Winter School on Energy Frontier hosted by the Korea Institute for Advanced Study. They extend lectures presented at the 2017 and 2018 CTEQ summer schools in [1] and the 2020 Hadron Collider Physics Summer School hosted by Fermilab in [2]. Jets in quantum chromodynamics (QCD) are motivated from familiar results in classical electricity and magnetism, through identification of structures that exhibit approximate scale invariance. From this point, an effective description of QCD jets is developed from simple assumptions that necessitate all-orders resummation for obtaining finite results. With machine learning becoming an increasingly important tool of particle physics, I discuss its utility exclusively from the biased view for increasing human knowledge. A simple argument that the likelihood for quark versus gluon discrimination is infrared and collinear safe is presented as an example of this approach. End-of-lecture exercises are also provided.

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“…In principle, one can distinguish between these two kinds of b-jets using an appropriate machine learning technique for gluon-quark jet classification. However, this is not straightforward and should be done carefully [8,9].…”

Section: Motivationmentioning

confidence: 99%

Discriminating the heavy jet production mechanisms in associated $Z$ + heavy flavor events at the LHC

Baranov,

Martinez,

Jung

et al. 2021

Preprint

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We reconsider the associated Z boson and charm or beauty jet production at the LHC with paying special attention to the formation dynamics of heavy jets. Two different approaches are studied: first one, where heavy quarks are produced in the hard scattering subprocesses, implemented in the Monte-Carlo generator pegasus, and another scenario, where the hard scattering is calculated at NLO with MadGraph5 aMC@NLO and TMD parton shower is included (implemented in the Monte-Carlo generator Cascade3). We compare the predictions obtained in both schemes with latest experimental data for associated Z +b production cross sections and the relative production rate σ(Z +c)/σ(Z + b) collected by the ATLAS and CMS Collaborations at √ s = 13 TeV. We introduce two kinematic observables (denoted as z b and p rel T ) which can be used to discriminate the heavy jet production mechanisms. Using these variables we trace the shape of the simulated b-jet events and recommend that these observables be taken into consideration in the forthcoming experimental analyses.

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Safety of Quark/Gluon Jet Classification

Cited by 17 publications

References 41 publications

Phenomenology of jet angularities at the LHC

Phenomenology of jet angularities at the LHC

Jet Physics from the Ground Up

Discriminating the heavy jet production mechanisms in associated $Z$ + heavy flavor events at the LHC

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