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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
| Authors (as registered SciPost users): | Tilman Plehn · Jennifer Thompson |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/1812.09223v2 (pdf) |
| Date accepted: | 2019-05-27 |
| Date submitted: | 2019-05-21 02:00 |
| Submitted by: | Thompson, Jennifer |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| Approach: | Theoretical |
Abstract
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.
Author comments upon resubmission
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
searches.
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.
Published as SciPost Phys. 6, 069 (2019)