Speaker
Ranit Das
(Heidelberg University)
Description
Simulation-based inference (SBI) is a powerful tool for likelihood-free parameter estimation, but often requires large numbers of simulated events. At the LHC, where event generation can be computationally expensive, particularly with restrictive generator-level cuts and detector simulation, this can become a major bottleneck. We demonstrate sequential SBI for the inference of resonance masses and widths, showing that it can substantially improve sample efficiency by adaptively concentrating simulations in regions of parameter space favored by the data. This enables accurate inference with significantly fewer simulated events, making SBI more practical for computationally demanding LHC analyses.
Authors
Henning Bahl
(Heidelberg University)
Ranit Das
(Heidelberg University)