Kavli Affiliate: Mihoko M. Nojiri
| First 5 Authors: Rafał Masełek, Mihoko M. Nojiri, Kazuki Sakurai, ,
| Summary:
The system of light electroweakinos and heavy squarks gives rise to one of
the most challenging signatures to detect at the LHC. It consists of missing
transverse energy recoiled against a few hadronic jets originating either from
QCD radiation or squark decays. The analysis generally suffers from the large
irreducible Z + jets $(Z to nu bar nu)$ background. In this study, we
explore Machine Learning (ML) methods for efficient signal/background
discrimination. Our best attempt uses both reconstructed (jets, missing
transverse energy, etc.) and low-level (particle-flow) objects. We find that
the discrimination performance improves as the pT threshold for soft particles
is lowered from 10 GeV to 1 GeV, at the expense of larger systematic
uncertainty. In many cases, the ML method provides a factor two enhancement in
$S/sqrt{(S + B)}$ from a simple kinematical selection. The sensitivity on the
squark-elecroweakino mass plane is derived with this method, assuming the Run-3
and HL-LHC luminosities. Moreover, we investigate the relations between input
features and the network’s classification performance to reveal the physical
information used in the background/signal discrimination process.
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