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Reconstructing sparticle masses at the LHC using generative machine learning
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DOI：10.1140/epjs/s11734-025-02015-x.png)
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Abstract
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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, $$pp \rightarrow \tilde{\chi }_1^{\pm }\tilde{\chi }_2^0$$ , 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 $$pp\rightarrow \tilde{\chi }_1^{\pm }\tilde{\chi }_1^{\mp }+\tilde{\chi }_1^{\pm }\tilde{\chi }_2^0$$ pair production at the HL-LHC in the channel, and for a fixed value of $$m_{\tilde{\chi }_2^0}$$ , we obtain reconstruction efficiencies $$\gtrsim 80\%$$ over a wide range of $$m_{\tilde{\chi }_1^0}$$ for $$\Delta m = 30~$$ GeV.
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