Performance versus Resilience in Modern Quark-Gluon Tagging

Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propo...

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Bibliographic Details
Published inarXiv.org
Main Authors Butter, Anja, Dillon, Barry M, Plehn, Tilman, Vogel, Lorenz
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.12.2022
Subjects
Bayesian analysis
Gluons
Interpolation
Optimization
Quarks
Resilience
Stability analysis
Training
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Summary:Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in Pythia and Herwig simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propose conditional training on interpolated samples, combined with a controlled Bayesian network, as a more resilient framework. The interpolation parameter can be used to optimize the training evaluated on a calibration dataset, and to test the stability of this optimization. The interpolated training might also be useful to track generalization errors when training networks on simulation.
ISSN:2331-8422