Probing sub-TeV Higgsinos aided by a machine-learning-based top tagger in the context of trilinear R -parity violating SUSY
Year: 2025
Type: article
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 boosted decision tree 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 HL-LHC.
Authors: Rajneil Baruah, Arghya Choudhury, Kirtiman Ghosh, Subhadeep Mondal, Rameswar Sahu
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410191722