Towards a method to anticipate dark matter signals with deep learning at the LHC
We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large...
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Published in | SciPost physics Vol. 12; no. 2; p. 063 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
SciPost
01.02.2022
|
Online Access | Get full text |
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Summary: | We study several simplified dark matter (DM) models and their
signatures at the LHC using neural networks. We focus on the usual
monojet plus missing transverse energy channel, but to train the
algorithms we organize the data in 2D histograms instead of
event-by-event arrays. This results in a large performance boost to
distinguish between standard model (SM) only and SM plus new physics
signals. We use the kinematic monojet features as input data which allow
us to describe families of models with a single data sample. We found
that the neural network performance does not depend on the simulated
number of background events if they are presented as a function of
S/\sqrt{B}
S
/
B
,
for reasonably large
B
B
,
where
S
S
and
B
B
are the number of signal and background events per histogram,
respectively. This provides flexibility to the method, since testing a
particular model in that case only requires knowing the new physics
monojet cross section. Furthermore, we also discuss the network
performance under incorrect assumptions about the true DM nature.
Finally, we propose multimodel classifiers to search and identify new
signals in a more general way, for the next LHC run. |
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ISSN: | 2542-4653 2542-4653 |
DOI: | 10.21468/SciPostPhys.12.2.063 |