Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events
Abstract
This study focuses on the novel application of a normalizing flow as a method of domain adaptation. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a method of transforming latent representations of physics data to a normal distribution and then to a physics distribution again. The final distribution models a simulated distribution. Following the transformation process, the data can be classified by a neural network trained on labeled simulation data. The present study succeeds in training two normalizing flows that can transform between data (or simulation) and a Gaussian distribution.
Cite
@article{arxiv.2403.14804,
title = {Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events},
author = {Rowan Kelleher and Anselm Vossen},
journal= {arXiv preprint arXiv:2403.14804},
year = {2024}
}
Comments
To be published in JINST as part of proceedings for AI4EIC2023. 6 pages, 3 figures