Speaker
Description
We are exploring the application of the IRIS-HEP Simulation-Based Inference (SBI) toolkit to precision Higgs measurements. In this talk, we discuss methodological developments aimed at improving robustness and of SBI workflows in realistic LHC settings.
On the tooling side, we explore physics-informed inductive biases in neural architectures, energy-conserving optimization schemes as alternatives to Adam, pre-training strategies for improving computing efficiency, and application of SBI to precision cross-section measurements (e.g. OmniFold). On the modeling side, we discuss strategies to mitigate Monte Carlo statistical uncertainties through the wifi ensembling approach.
The goal is to identify practical improvements that strengthen SBI applications in precision LHC physics and to foster collaboration between methodological and experimental communities.