Abstract

We show that density models describing multiple observables with (1) hard boundaries and (2) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable spectra are deformed by hypothesis variations, and is made more expressive by projecting data onto a configurable latent space. It may be used as a statistical model for scientific discovery in interpreting experimental observations, for example when constraining the parameters of a physical model or tuning simulation parameters according to calibration data. The model may also be sampled for use within a Monte Carlo simulation chain, or used to estimate likelihood ratios for event classification. The method is demonstrated on simulated high-energy particle physics data considering the anomalous electroweak production of a Z boson in association with a dijet system at the Large Hadron Collider, and the accuracy of inference is tested using a realistic toy example. The developed methods are domain agnostic; they may be used within any field to perform simulation or inference where a dataset consisting of many real-valued observables has conditional dependence on external parameters.

Details

Title
Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences
Author
Menary, Stephen B 1   VIAFID ORCID Logo  ; Price, Darren D 1   VIAFID ORCID Logo 

 Department of Physics & Astronomy, University of Manchester , Manchester, United Kingdom 
First page
015021
Publication year
2022
Publication date
Mar 2022
Publisher
IOP Publishing
e-ISSN
26322153
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2624572238
Copyright
© 2022 The Author(s). Published by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.