Sequential SBI for Resonance Searches at the LHC

Abstract

Neural simulation-based inference has been shown to extract significantly more information from LHC data than histogram-based analyses. So far, it has mostly been applied to couplings, for which the polynomial structure of the scattering amplitude can be exploited. Masses and widths instead move and deform the signal in phase space, and learning their likelihood requires many event samples covering the continuous theory parameter space. We show how sequential simulation-based inference can address this by iteratively concentrating the simulations where the observed data points. Using a parton-level example with an exact reference likelihood and reconstruction-level analyses of $A\to ZH\to \ell\ell b\bar b$ and $Y\to HH\to (t\bar t)(t\bar t)$, we demonstrate that sequential training produces significantly more accurate and better calibrated likelihoods than single round training with the same amount of training events.