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Related Experiment Video

Updated: May 9, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

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

Atlas Collaboration¹, James Howarth²

¹European Organization for Nuclear Research, HCP, CH-1211 GENEVE 23, Geneva, CH-1211, SWITZERLAND.

Reports on Progress in Physics. Physical Society (Great Britain)

|May 2, 2025

Summary

Neural simulation-based inference (NSBI) offers advanced statistical inference for high-dimensional data, like at the Large Hadron Collider. This method enhances sensitivity by avoiding data binning and incorporating systematic uncertainties.

Keywords:

Machine learning frequentist statistics likelihood-free inference neural simulation-based inference parameter inference

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Area of Science:

High Energy Physics
Machine Learning
Statistical Inference

Background:

Traditional statistical methods can lose sensitivity when analyzing complex, high-dimensional datasets common in particle physics.
Binning data into histograms, a standard practice, may not be optimal for all theoretical phase spaces or can lead to information loss.

Purpose of the Study:

To develop and validate a robust Neural Simulation-Based Inference (NSBI) framework for statistical inference in high-dimensional parameter estimation.
To extend NSBI for application in full-scale analyses, including handling systematic uncertainties and finite event counts.

Main Methods:

Utilized neural networks to estimate probability density ratios, a core component of the NSBI framework.
Incorporated a comprehensive treatment of systematic uncertainties and quantified uncertainties arising from finite training sample sizes.
Developed methods for constructing confidence intervals and implemented diagnostic checks for validating the framework's robustness.

Main Results:

Demonstrated the power and feasibility of the NSBI framework on simulated data for an off-shell Higgs boson couplings measurement.
Successfully applied the method to a complex scenario involving four-lepton final states, showcasing its capability to handle intricate physics analyses.
Validated the robustness of the NSBI approach through intermediate diagnostic checks.

Conclusions:

The developed NSBI framework provides a powerful extension to standard statistical methodologies used at the Large Hadron Collider.
This approach enhances statistical inference for high-dimensional data, offering improved sensitivity and broader applicability in particle physics analyses.
The method is particularly beneficial for measurements where traditional histogramming techniques may be suboptimal.