Reconstructing Sparticle masses at the LHC using Generative Machine Learning

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, $pp\to {\tilde{\chi }}_1^{\pm }{\tilde{\chi }}_2^0$, in the $1\mathrm{\ell }+2\gamma +jets$ channel at the high luminosity LHC~(HL-LHC). We find that our framework can achieve mass reconstruction efficiency of $\gtrsim 70\mathrm{\%}$ for the lightest neutralino ${\tilde{\chi }}_1^0$ and $\gtrsim 40\mathrm{\%}$ for the second lightest neutralino ${\tilde{\chi }}_2^0$, for a mass tolerance of $\mathrm{\Delta }m=30$GeV, across the entire parameter space accessible at the HL-LHC. We further extend our analysis to a different scenario with $pp\to {\tilde{\chi }}_1^{\pm }{\tilde{\chi }}_1^{\mp }+{\tilde{\chi }}_1^{\pm }{\tilde{\chi }}_2^0$ pair production at the HL-LHC in the $4\mathrm{\ell }+\mathrm{E}{\text{\hspace{0.17em}⁣}\text{\hspace{0.17em}⁣}\text{\hspace{0.17em}⁣}\mathrm{/}}_{\mathrm{T}}$ channel, and for a fixed value of $m_{{\tilde{\chi }}_2^0}$, we obtain reconstruction efficiencies $\gtrsim 80\mathrm{\%}$ over a wide range of $m_{{\tilde{\chi }}_1^0}$ for $\mathrm{\Delta }m=30$GeV.