Revisiting the Electroweakino Sector of the Baryon Number Violating MSSM at the HL-LHC with Deep Neural Networks
Rahool Kumar Barman, Arghya Choudhury, and Subhadeep Sarkar

TL;DR
This paper uses deep neural networks to project the HL-LHC's sensitivity to electroweakino production in R-parity violating MSSM scenarios, exploring various decay channels and operators to identify potential discovery reach.
Contribution
It introduces a machine learning approach with multi-layer perceptrons to improve sensitivity projections for electroweakino detection in RPV MSSM at the HL-LHC, considering multiple decay channels and operators.
Findings
HL-LHC can probe winos up to 900 GeV with certain operators.
Neural networks enhance signal-background discrimination.
Projected sensitivities vary with RPV operators and decay channels.
Abstract
We study the projected sensitivity of direct electroweakino production at the HL-LHC in a simplified framework with wino-like, mass degenerate and , and a bino-like lightest neutralino , assuming R-parity violating~(RPV) through the baryon number violating and operators. We consider three channels with the RPV operator: mediated , mediated , and mediated . In each channel, we train benchmark-specific multi-layer perceptrons (MLPs), analogous to signal-region classifiers, on the four-momenta of the final state particles along…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · High-Energy Particle Collisions Research
