Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
Abstract
There are many cases in collider physics and elsewhere where a calibration dataset is used to predict the known physics and / or noise of a target region of phase space. This calibration dataset usually cannot be used out-of-the-box but must be tweaked, often with conditional importance weights, to be maximally realistic. Using resonant anomaly detection as an example, we compare a number of alternative approaches based on transporting events with normalizing flows instead of reweighting them. We find that the accuracy of the morphed calibration dataset depends on the degree to which the transport task is set up to carry out optimal transport, which motivates future research into this area.
Cite
@article{arxiv.2212.06155,
title = {Efficiently Moving Instead of Reweighting Collider Events with Machine Learning},
author = {Radha Mastandrea and Benjamin Nachman},
journal= {arXiv preprint arXiv:2212.06155},
year = {2022}
}
Comments
7 pages, 3 figures. Presented at the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS)