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.
| Origin | Files produced by the author(s) |
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