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

TL;DR

This work tackles the challenge of discovering higgsinos at the LHC within a baryon-number-violating RPV SUSY framework, focusing on the mass range $400$ GeV to $1000$ GeV. The authors employ a machine-learning boosted-top tagger based on LorentzNet, enhanced by tracker information, to identify boosted top jets from higgsino decays in events with multiple $b$-jets and light jets. They construct two independent signal regions and train two BDT classifiers to distinguish signal from SM backgrounds, projecting HL-LHC reach with $\sqrt{s}=14$ TeV and $L=3000$ fb$^{-1}$. The analysis shows a potential mass reach up to $\sim 925$ GeV in the zero-background-uncertainty case, with sensitivity diminishing as systematic uncertainties grow, and demonstrates the value of mass-matched training and multi-region ML strategies for SUSY searches. Overall, the study expands the exploration of sub-TeV higgsinos in the RPV context by leveraging advanced top-tagging and ML techniques to achieve improved sensitivity at the HL-LHC.

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.

Figures (8)

• Figure 1: Production and decay modes of the pure higgsino states in the simplified model considered in this work.
• Figure 2: Distributions for the combined signal with $m_{\widetilde{\chi}_1^0}= m_{\widetilde{\chi}_1^{\pm}} =m_{\widetilde{\chi}_2^0}=700$ GeV and background events. From left to right, we have the $M_{eff}$, Reconstructed Mass and $M_{t^\prime}$ for SR1.
• Figure 3: Distributions for the combined signal with $m_{\widetilde{\chi}_1^0}= m_{\widetilde{\chi}_1^{\pm}} =m_{\widetilde{\chi}_2^0}=700$ GeV and background events. From left to right, we have the $M_{t^\prime}$, $M_{eff}$ and $P_T^{j_1}$ for SR2.
• Figure 4: Distributions for the combined signal events with $m_{\widetilde{\chi}_1^0}=700$ GeV and background events as a function of BDT score for the signal regions SR1(left) and SR2(right).
• Figure 5: Median expected exclusion significance for the RPV frameworks with Higgsino pair production at the future HL-LHC ($\sqrt{s}=14$ TeV, $\cal{L}$ = 3000 $fb^{-1}$). Here the classifiers are tested and trained on the same mass of Higgsino for the signal events for each benchmark point. The blue, red, green, and violet colors correspond to expected exclusion contours with 0%, 1%, 3%, and 5% background uncertainties.