Normalizing flows for domain adaptation when identifying Λ hyperon events
Journal Article
·
· Journal of Instrumentation
- Duke Univ., Durham, NC (United States)
- Duke Univ., Durham, NC (United States); Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Here this study focuses on the application of a normalizing flow as a method of domain adaptation when classifying physics data. Normalizing flows offer a way to transform data points between two different distributions. The present study investigates a novel 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. After being transformed, 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.
- Research Organization:
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Nuclear Physics (NP)
- Grant/Contract Number:
- AC05-06OR23177
- OSTI ID:
- 2441486
- Report Number(s):
- JLAB-PHY--24-4185; arXiv:2403.14804; DOE/OR/23177-7685
- Journal Information:
- Journal of Instrumentation, Journal Name: Journal of Instrumentation Journal Issue: 06 Vol. 19; ISSN 1748-0221
- Publisher:
- Institute of Physics (IOP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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