End-to-end jet classification of quarks and gluons with the CMS Open Data - INSPIRE

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End-to-end jet classification of quarks and gluons with the CMS Open Data

M. Andrews
(
- Carnegie Mellon U.
)
,
J. Alison
(
- Carnegie Mellon U.
)
,
S. An
(
- Carnegie Mellon U. and
- CERN
)
,
Patrick Bryant
,
B. Burkle
(
- Brown U.
)

Feb 21, 2019

8 pages

Published in:

Nucl.Instrum.Meth.A 977 (2020) 164304

Published: Oct 11, 2020

e-Print:

1902.08276 [hep-ex]

DOI:

10.1016/j.nima.2020.164304

View in:

reference search44 citations

Citations per year

Abstract: (Elsevier)

We describe the construction of novel end-to-end jet image classifiers to discriminate quark- versus gluon-initiated jets using the simulated CMS Open Data. These multi-detector images correspond to true maps of the low-level energy deposits in the detector, giving the classifiers direct access to the maximum recorded event information about the jet, differing fundamentally from conventional jet images constructed from reconstructed particle-level information. Using this approach, we achieve classification performance competitive with current state-of-the-art jet classifiers that are dominated by particle-based algorithms. We find the performance to be driven by the availability of precise spatial information, highlighting the importance of high-fidelity detector images. We then illustrate how end-to-end jet classification techniques can be incorporated into event classification workflows using Quantum Chromodynamics di-quark versus di-gluon events. We conclude with the end-to-end event classification of full detector images, which we find to be robust against the effects of underlying event and pileup outside the jet regions-of-interest.

Note:

10 pages, 5 figures, 7 tables; v2: published version

Machine learning
Jet images
End-to-end
CMS Open Data
Convolutional neural network
LHC
p p: scattering
p p: colliding beams
quark: jet
gluon: jet

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Figures(19)

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