Deep Learning Discovery Algorithms in High-Energy Physics and the Conflict Between Model-Independence and Robustness

Erkenntnis

Abstract

Abstract Machine Learning models have become widely recognized for their potential to revolutionize science. In High-Energy Physics, they are of particular promise in propelling the field forward by fueling experimental discoveries. State-of-the-art uses of Machine Learning are considered ‘model-independent’ by the High-Energy Physics community; a verdict shared by several philosophers. In this paper, we dig into the details of unsupervised anomaly detection and show that even modest senses of model-independence are doubt-worthy, once considerations of robustness are factored in. We also offer reasons to think that the theme generalizes beyond the narrow context of High-Energy Physics.

Authors 3

1. Florian J. Boge corresponding

   TU Dortmund University

   Affiliation as printed

   Institute for Philosophy and Political Science, TU Dortmund University & Lamarr Institute for Machine Learning and Artificial Intelligence, Emil-Figge-Str. 540 Room 2.247, 44227, Dortmund, Germany

2. M. Krämer Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

   Institute for Theoretical Particle Physics and Cosmology, RWTH Aachen University, Aachen, Germany

3. Christian Zeitnitz

   University of Wuppertal

   Affiliation as printed

   Working Group Experimental Particle Physics, Wuppertal University, Wuppertal, Germany