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Sten is a caucasian male with cropped red hair and beard.

Sten Åstrand

Doctoral student

Sten is a caucasian male with cropped red hair and beard.

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

Author

  • G. Aad
  • T.P.A. Åkesson
  • K.S.V. Astrand
  • C. Doglioni
  • P.A. Ekman
  • V. Hedberg
  • H. Herde
  • B. Konya
  • E. Lytken
  • R. Poettgen
  • O. Smirnova
  • E.J. Wallin
  • L. Zwalinski
Show all

Summary, in English

Neural simulation-based inference (NSBI) 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 NSBI 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. © 2025 The Author(s). Published by IOP Publishing Ltd.

Department/s

  • Particle and nuclear physics
  • eSSENCE: The e-Science Collaboration
  • Department of Physics

Publishing year

2025

Language

English

Publication/Series

Reports on Progress in Physics

Volume

88

Issue

6

Document type

Journal article

Publisher

IOP Publishing

Topic

  • Subatomic Physics

Keywords

  • frequentist statistics
  • likelihood-free inference
  • machine learning
  • neural simulation-based inference
  • parameter inference

Status

Published

ISBN/ISSN/Other

  • ISSN: 0034-4885