# Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

arXiv: https://arxiv.org/abs/2005.00568 · HTML: https://mlsanity.com/paper/2005.00568/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

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- [HateDebias: On the Diversity and Variability of Hate Speech Debiasing](https://mlsanity.com/paper/2406.04876/) (2024)

## Abstract (arXiv)

We apply adversarial domain adaptation in unsupervised setting to reduce sample bias in a supervised high energy physics events classifier training. We make use of a neural network containing event and domain classifier with a gradient reversal layer to simultaneously enable signal versus background events classification on the one hand, while on the other hand minimising the difference in response of the network to background samples originating from different MC models via adversarial domain classification loss. We show the successful bias removal on the example of simulated events at the LHC with tbartH signal versus tbartbbarb background classification and discuss implications and limitations of the method