Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC

TL;DR

This paper enhances the sensitivity of RPV SUSY searches at the LHC by applying machine learning techniques to multilepton final states, significantly improving gaugino mass exclusion limits compared to traditional methods.

Contribution

It introduces a machine learning-based analysis for RPV SUSY searches, achieving higher sensitivity and setting new projected exclusion limits on gaugino masses at future colliders.

Findings

ML analysis improves sensitivity over cut-based methods.

Projected exclusion limits reach up to 4 TeV at HE-LHC.

Final states with ≥4 leptons are most sensitive for probing gaugino masses.

Abstract

In this work, we have explored the sensitivity of multilepton final states in probing the gaugino sector of R-parity violating supersymmetric scenario with specific lepton number violating trilinear couplings ($\lambda_{ijk}$) being non-zero. The gaugino spectrum is such that the charged leptons in the final state can arise from the R-parity violating decays of the lightest supersymmetric particle (LSP) as well as R-parity conserving decays of the next-to-LSP (NLSP). Apart from a detailed cut-based analysis, we have also performed a machine learning-based analysis using boosted decision tree algorithm which provides much better sensitivity. In the scenarios with non-zero $\lambda_{121}$ and/or $\lambda_{122}$ couplings, the LSP pair in the final states decays to $4l~(l = e, \mu) + \rm E{\!\!\!/}_T$ final states with $100\%$ branching ratio. We have shown that under this circumstance, a final state with $\ge 4l$ has the highest sensitivity in probing the gaugino masses. We also discuss how the sensitivity can change in the presence of $\tau$ lepton(s) in the final state due to other choices of trilinear couplings. We present our results through the estimation of the discovery and exclusion contours in the gaugino mass plane for both the HL-LHC and the HE-LHC. For $\lambda_{121}$ and/or $\lambda_{122}$ nonzero scenario, the projected 2$\sigma$ exclusion limit on NLSP masses reaches upto 2.37 TeV and 4 TeV for the HL-LHC and the HE-LHC respectively by using a machine learning based algorithm. We obtain an enhancement of $\sim$ 380 (190) GeV in the projected 2$\sigma$ exclusion limit on the NLSP masses at the 27 (14) TeV LHC. Considering the same final state ($N_l \geq 4$) for $\lambda_{133}$ and/or $\lambda_{233}$ non-zero scenario, we find that the corresponding 2$\sigma$ projected limits are $\sim$ 1.97 TeV and $\sim$ 3.25 TeV for the HL-LHC and HE-LHC respectively.