Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY

TL;DR

This paper demonstrates that using machine learning techniques, specifically a top tagger and BDT classifier, can significantly improve the detection prospects of sub-TeV higgsinos at the LHC, reaching masses up to 925 GeV.

Contribution

The study introduces a novel ML-based top tagging approach combined with a BDT classifier to enhance higgsino detection sensitivity in RPV SUSY scenarios at the LHC.

Findings

Higgsino masses up to 925 GeV can be probed at the high luminosity LHC.

ML techniques improve signal discrimination over SM backgrounds.

Two optimized signal regions increase detection prospects.

Abstract

Probing higgsinos remains a challenge at the LHC owing to their small production cross-sections and the complexity of the decay modes of the nearly mass degenerate higgsino states. The existing limits on higgsino mass are much weaker compared to its bino and wino counterparts. This leaves a large chunk of sub-TeV supersymmetric parameter space unexplored so far. In this work, we explore the possibility of probing higgsino masses in the 400 - 1000 GeV range. We consider a simplified supersymmetric scenario where R-Parity is violated through a baryon number violating trilinear coupling. We adopt a machine learning-based top tagger to tag the boosted top jets originating from higgsinos, and for our collider analysis, we use a BDT classifier to discriminate signal over SM backgrounds. We construct two signal regions characterized by at least one top jet and different multiplicities of $b$-jets and light jets. Combining the statistical significance obtained from the two signal regions, we show that higgsino mass as high as 925 GeV can be probed at the high luminosity LHC.