Reconstructing Sparticle masses at the LHC using Generative Machine Learning

TL;DR

This paper introduces a generative machine learning framework combining transformers and diffusion networks to reconstruct heavy particle masses at the LHC, achieving high efficiency across broad parameter spaces.

Contribution

The novel framework effectively infers sparticle masses from detector data, demonstrating significant improvements in reconstruction efficiency over existing methods.

Findings

Achieves over 70% mass reconstruction efficiency for neutralinos at HL-LHC.

Extends to multiple supersymmetry scenarios with over 80% efficiency.

Operates effectively across the entire accessible parameter space.

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, $pp \to \tilde\chi_1^{\pm} \tilde\chi_2^0$, in the $1\ell + 2\gamma + jets$ channel at the high luminosity LHC~(HL-LHC). We find that our framework can achieve mass reconstruction efficiency of $\gtrsim 70\%$ 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 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 $4\ell+\rm E{\!\!\!/}_T$ channel, and for a fixed value of $m_{\tilde\chi_2^0}$, we obtain reconstruction efficiencies $\gtrsim80\%$ over a wide range of $m_{\tilde\chi_1^0}$ for $\Delta m = 30~$GeV.