Systematic aware learning
A case study in High Energy Physics
LRI/TAU, Univ. Paris-Sud/INRIA/CNRS, Université Paris-Saclay,
2 LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France
3 ChaLearn Berkeley, USA
Published online: 17 September 2019
Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient representation that is insensitive to perturbations induced by the systematic effects. We present an experimental comparison of the adversarial knowledge-free approach and a less data-intensive alternative.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.