Deep generative models for generating Drell-Yan events in the ATLAS collaboration at the LHC
Date:
The search for the dimuon decay of the Standard Model (SM) Higgs boson looks for a tiny peak on top of a smoothly falling SM background in the dimuon invariant mass spectrum $m_{\mu\mu}$. Due to the very small signal-to-background ratio, which is at the level of 0.2% in the region $120 < m_{\mu\mu} < 130$ GeV for an inclusive selection, an accurate determination of the background is of paramount importance. The $m_{\mu\mu}$ background spectrum is parameterised by analytic functions that can describe this distribution at the per-mill level to avoid a significant bias in the extracted signal yields. The criteria used to select the background functions are based on the spurious signal, which measures the residual signal events obtained from signal-plus-background fits to background-only MC templates. Therefore, these MC templates have to be derived from events with very high statistics in order to reduce possible fluctuations. Computationally, it is extremely expensive, if not impossible, to generate the Drell-Yan $Z/\gamma^* \to \mu\mu$ background events with detailed simulation. Our study focuses on the use of generative models, trained on the existing fully simulated events of the ATLAS experiment in order to generate billions of events using GPUs for the spurious signal study, and to test the statistical independence of these events. This study presents an interesting alternative procedure in for the generation of events with high statistical power that could be used in the future by default in many analyses at the LHC.