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Title: Normalizing flows for domain adaptation when identifying Λ hyperon events

Journal Article · · Journal of Instrumentation
ORCiD logo [1];  [2]
  1. Duke Univ., Durham, NC (United States)
  2. 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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journal April 2020
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Normalizing Flows: An Introduction and Review of Current Methods journal January 2020
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Improving Λ Signal Extraction with Domain Adaptation via Normalizing Flows conference July 2024