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20162025
most citedDeep learning in color: towards automated quark/gluon jet discrimination

291 citations · 1.8k across the 21 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

hep-ph2019★ 171 cited

OmniFold: A Method to Simultaneously Unfold All Observables

Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev +2

Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments. Unfolding is traditiona…

hep-ph2019★ 20 cited

Cutting Multiparticle Correlators Down to Size

Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler

Multiparticle correlators are mathematical objects frequently encountered in quantum field theory and collider physics. By translating multiparticle correlators into the language o…

hep-ph2019★ 65 cited

Exploring the Space of Jets with CMS Open Data

Patrick T. Komiske, Radha Mastandrea, Eric M. Metodiev +2

We explore the metric space of jets using public collider data from the CMS experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at the Large Hadron Collide…

hep-ph2019★ 240 cited

The Machine Learning Landscape of Top Taggers

G. Kasieczka, T. Plehn, A. Butter +24

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…

hep-ph2019★ 123 cited

The Metric Space of Collider Events

Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler

When are two collider events similar? Despite the simplicity and generality of this question, there is no established notion of the distance between two events. To address this que…