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This content will become publicly available on September 1, 2026

Title: Frequentist uncertainties on neural density ratios with wi fi ensembles
We introduce w i f i ensembles as a novel framework to obtain asymptotic frequentist uncertainties on density ratios, with a particular focus on neural ratio estimation in the context of high-energy physics. When the density ratio of interest is a likelihood ratio conditioned on parameters, w i f i ensembles can be used to perform simulation-based inference on those parameters. After training the basis functions f i ( x ) , uncertainties on the weights w i can be straightforwardly propagated to the estimated parameters without requiring extraneous bootstraps. To demonstrate this approach, we present an application in quantum chromodynamics at the Large Hadron Collider, using w i f i ensembles to estimate the likelihood ratio between generated quark and gluon jets. We use this learned likelihood ratio to estimate the quark fraction in a synthetic mixed quark/gluon sample, showing that the resultant uncertainties empirically satisfy the desired coverage properties.  more » « less
Award ID(s):
2019786
PAR ID:
10648149
Author(s) / Creator(s):
;
Publisher / Repository:
American Physical Society
Date Published:
Journal Name:
Physical Review D
Volume:
112
Issue:
5
ISSN:
2470-0010
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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