An implementation of neural simulation-based inference for parameter estimation in ATLAS

TL;DR

This paper presents a neural simulation-based inference framework for parameter estimation in high-energy physics, capable of handling complex uncertainties and providing robust confidence intervals, demonstrated on Higgs boson coupling simulations.

Contribution

It introduces a scalable neural inference method that incorporates systematic uncertainties and validation diagnostics for large-scale physics analyses.

Findings

Effective on simulated Higgs data

Handles systematic uncertainties robustly

Provides validated confidence intervals

Abstract

Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a neural simulation-based inference framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.