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Conference Papers Year : 2017

Adversarial learning to eliminate systematic errors: a case study in High Energy Physics

Abstract

Making the region selection procedure used in High Energy Physics analysis robust to systematic errors is a case of supervised domain adaptation. This paper proposes a benchmark that captures a simple but realistic case of systematic HEP analysis, in order to expose the issue to the wider community. The benchmark makes easy to conduct an experimental comparison of the recent adversarial knowledge-free approach and a less data-intensive alternative.
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Dates and versions

hal-01665925 , version 1 (17-12-2017)

Identifiers

  • HAL Id : hal-01665925 , version 1

Cite

Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau. Adversarial learning to eliminate systematic errors: a case study in High Energy Physics. NIPS 2017 - workshop Deep Learning for Physical Sciences, Dec 2017, Long Beach, United States. pp.1-5. ⟨hal-01665925⟩
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