BibTeX

@article{2506.00119v1,
Author        = {Chi Lung Cheng and Ranit Das and Runze Li and Radha Mastandrea and Vinicius Mikuni and Benjamin Nachman and David Shih and Gup Singh},
Title         = {Generator Based Inference (GBI)},
Eprint        = {2506.00119v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {Statistical inference in physics is often based on samples from a generator
(sometimes referred to as a ``forward model") that emulate experimental data
and depend on parameters of the underlying theory. Modern machine learning has
supercharged this workflow to enable high-dimensional and unbinned analyses to
utilize much more information than ever before. We propose a general framework
for describing the integration of machine learning with generators called
Generator Based Inference (GBI). A well-studied special case of this setup is
Simulation Based Inference (SBI) where the generator is a physics-based
simulator. In this work, we examine other methods within the GBI toolkit that
use data-driven methods to build the generator. In particular, we focus on
resonant anomaly detection, where the generator describing the background is
learned from sidebands. We show how to perform machine learning-based parameter
estimation in this context with data-derived generators. This transforms the
statistical outputs of anomaly detection to be directly interpretable and the
performance on the LHCO community benchmark dataset establishes a new
state-of-the-art for anomaly detection sensitivity.},
Year          = {2025},
Month         = {May},
Url           = {http://arxiv.org/abs/2506.00119v1},
File          = {2506.00119v1.pdf}
}