BibTeX

@article{2506.00113v2,
Author        = {Sean Benevedes and Jesse Thaler},
Title         = {Frequentist Uncertainties on Neural Density Ratios with wifi Ensembles},
Eprint        = {2506.00113v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {hep-ph},
Abstract      = {We introduce wifi 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, wifi 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 wifi 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.},
Year          = {2025},
Month         = {May},
Url           = {http://arxiv.org/abs/2506.00113v2},
File          = {2506.00113v2.pdf}
}