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Quark-Gluon Tagging: Machine Learning vs Detector

by Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson

This Submission thread is now published as

Submission summary

As Contributors: Tilman Plehn · Jennifer Thompson
Arxiv Link: (pdf)
Date accepted: 2019-05-27
Date submitted: 2019-05-21 02:00
Submitted by: Thompson, Jennifer
Submitted to: SciPost Physics
Academic field: Physics
  • High-Energy Physics - Phenomenology
Approach: Theoretical


Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.

Ontology / Topics

See full Ontology or Topics database.

Gluons Large Hadron Collider (LHC) Machine learning (ML) Tagging

Published as SciPost Phys. 6, 069 (2019)

Author comments upon resubmission

First of all, we would like to thank the referees for their careful
reading and for their comments. We agree that this paper essentially
has a negative bottom line, and we also agree that we cannot
enthusiastically report the impact of quark-gluon tagging on a
reference process. However, we believe that these finding should be
out in the public, and if only to encourage others to do better or to
drop quark-gluon tagging from the problems worth pursuing for LHC

List of changes

Because many of the referees addressed the same points, we have
collected them together for a combined response.

- We have changed the title to "Quark-Gluon Tagging: Machine Learning vs Detector".
- We added many references on quark-gluon tagging and on IR safety.
- We now refer to 1711.02633 for a similar study.
- We specified LoLa in the abstract and the introduction.
- We slightly modified the introduction of the two reference processes.
- We added a reference to the difference of q-g tagging based on Pythia vs Sherpa.
- We streamlined Sec.2.1.
- We expanded the discussion of Fig.2, relating our findings to the available literature.
- We expanded the discussion of Fig.3, including the relation to the incomplete and non-basis of observables.
- We added discussion of IR safety at the end of Sec.2.1 and now explicitly mention girth and C2 as safe.
- We now refer to the detailed comparison of architectures from 1810.05165 and make it clear that our new focus
is on detector effects. We do not believe that a comparison to more than the CNN of 1612.01551 would add to the conclusions of the paper.
- We clarified the caption of Fig.7, how we extract MC truth from our simiulation, and the discussion of Fig.8.
- We unfortunately have no way of estimating the exact effect of quark-gluon tagging on a specific di-jet resonance search,
but we relate it to event-level observables and a similar analysis.
- We have added a (blunt) bottom line to the mono-jet discussion, but we believe that the discussion should be kept in spite of the negative conclusion.
- We added a couple of references and clarifications as requested by the referees, including on pile-up.

A couple of points we could not change are:

- In the discussion of Fig.9 we already discuss the fact that for a stiff MET cut the quark-gluon tagging performance suffers.
- We are sorry, but adding reliable rejection efficiencies to Tab.1 would require us to use much more GPU power than we have.
But an example number is 1/FPR=9.3 @ TPR=0.3.

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