Speaker
Description
We explore a generative model framework to infer the masses of heavy particles from detector-level data across 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 $1\ell + 2\gamma + \text{jets}$ channel. We find that our framework can achieve mass reconstruction efficiency of $\gtrsim 70\%$ for the lightest neutralino $\tilde\chi_1^0$ and $\gtrsim 40\%$ for the second lightest neutralino $\tilde\chi_2^0$, for a mass tolerance of $\Delta m = 30~$GeV, across the entire parameter space accessible at the collider. 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 in the $4\ell+\rm E{\!\!\!/}_T$ channel, and for a fixed value of $m_{\tilde\chi_2^0}$, we obtain reconstruction efficiencies $\gtrsim80\%$ over a wide range of $\tilde\chi_1^{0}$ for $\Delta m = 30~$GeV.