Reconstructing Sparticle masses at the LHC using Generative Machine Learning

Abstract

We explore a generative model framework to infer the masses of heavy particles from detector-level data over a broad parameter space. Our model combines a transformer-based detector encoder and a diffusion neural network. We first apply our model to a new physics scenario involving the pair production of wino-like chargino-neutralino, $pp \to \tildeχ_1^{\pm} \tildeχ_2^0$, in the $1\ell + 2γ+ jets$ channel at the high luminosity LHC~(HL-LHC). We find that our framework can achieve mass reconstruction efficiency of $\gtrsim 70\%$ for the lightest neutralino $\tildeχ_1^0$ and $\gtrsim 40\%$ for the second lightest neutralino $\tildeχ_2^0$, for a mass tolerance of $Δm = 30~$GeV, across the entire parameter space accessible at the HL-LHC. We further extend our analysis to a different scenario with $pp\to\tildeχ_1^{\pm}\tildeχ_1^{\mp}+\tildeχ_1^{\pm}\tildeχ_2^0$ pair production at the HL-LHC in the $4\ell+\rm E{\!\!\!/}_T$ channel, and for a fixed value of $m_{\tildeχ_2^0}$, we obtain reconstruction efficiencies $\gtrsim80\%$ over a wide range of $m_{\tildeχ_1^0}$ for $Δm = 30~$GeV.