Probing compressed mass spectrum supersymmetry at the high-luminosity LHC with the vector boson fusion topology

We present a phenomenology study investigating pair production of supersymmetric charginos and neutralinos (“electroweakinos”) with the vector boson fusion (VBF) topology in proton-proton collisions at CERN’s Large Hadron Collider (LHC). We examine the compressed-mass spectrum phase space that has been traditionally challenging due to experimental constraints. The final states considered have two jets, large missing momentum, and one, two, or three low-momentum leptons. Different model scenarios are considered for the production and decays of the electroweakinos. A novel high-performance and interpretable sequential attention-based machine learning algorithm is employed for signal-background discrimination and is observed to significantly improve signal sensitivity over traditional methods. We report expected signal significances for integrated luminosities of 137, 300, and 3000 fb⁻¹ fb - 1 corresponding to the current data acquired at the LHC, expectation for the end of Run 3, and the expectation for the high-luminosity LHC. Notably, this is the first instance of a search methodology for compressed SUSY that simultaneously combines the VBF topology and low-momentum leptons within a machine learning framework. This methodology results in projected 95% confidence level bounds that cover chargino masses up to 1.1 TeV, within the R-parity conserving minimal supersymmetric standard model, assuming the so-called “Wino-Bino light slepton model” scenario where the lightest SUSY particle ("Equation missing" ) is Bino, the lightest chargino ( χ ₁± χ ~ 1 ± ) is Wino, "Equation missing" GeV, and decays of the lightest chargino proceed via sleptons, which are the next-to-lightest SUSY particles. This parameter space, currently beyond the reach of searches at the LHC, is traditionally challenging to explore due to significant Standard Model backgrounds and low signal cross sections.