Speaker
Description
Neural Simulation-Based Inference (NSBI) is a family of emerging techniques that allow statistical inference using high-dimensional data, even when the exact likelihoods are analytically intractable. The techniques rely on leveraging deep learning to directly build likelihood-based or posterior-based inference models using high-dimensional information. By not relying on hand-crafted, low-dimensional summary observables, NSBI can improve sensitivity for precision measurements and searches — as has been demonstrated across several scientific domains.
We review recent NSBI applications in ATLAS and CMS and focus on a key practical challenge for application of NSBI to full-scale LHC analyses: scalable treatment of $O(10^2)$ nuisance parameters encoding systematic uncertainties. Building on HistFactory-style interpolations, Gaussian-process surrogates and information-geometric approximations, we explore the development of NSBI models and fitting strategies that retain sensitivity while keeping training and inference tractable at the scale of LHC experiments.
Finally, to support adoption of these techniques in real analyses, we introduce nsbi-common-utils [1], an open source Python toolkit providing a reproducible, modular end-to-end workflow for NSBI: data preparation, model training, calibration, and statistical inference, steered by configuration files. The talk will go into the details of this library and on its scope and practical usage in high-energy physics analysis.