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Uncertainty Aware Learning for High Energy Physics With A Cautionary Tale
Aishik Ghosh · Benjamin Nachman

Machine learning tools provide a significant improvement in sensitivity over traditional analyses by exploiting subtle patterns in high-dimensional feature spaces. These subtle patterns may not be well-modeled by the simulations used for training machine learning methods, resulting in an enhanced sensitivity to systematic uncertainties. Contrary to the traditional wisdom of constructing an analysis strategy that is invariant to systematic uncertainties, we study the use of a classifier that is fully aware of uncertainties and their corresponding nuisance parameters. We show on two datasets that this dependence can actually enhance the sensitivity to parameters of interest compared to baseline approaches. Finally, we provide a cautionary example for situations where uncertainty mitigating techniques may serve only to hide the true uncertainties.

Author Information

Aishik Ghosh (UC Irvine)

Researching AI for physics. Working with OECD on AI policy, AI in science. Interests include AI Ethics.

Benjamin Nachman (Lawrence Berkeley National Laboratory)

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