Deep {Learning} {Discovery} {Algorithms} in {High}-{Energy} {Physics} and the {Conflict} {Between} {Model}-{Independence} and {Robustness}
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.
- Veröffentlicht in:
Erkenntnis - Typ:
Article - Autoren:
- Jahr:
2026 - Source:
https://doi.org/10.1007/s10670-026-01179-9
Informationen zur Zitierung
: Deep {Learning} {Discovery} {Algorithms} in {High}-{Energy} {Physics} and the {Conflict} {Between} {Model}-{Independence} and {Robustness}, Erkenntnis, 2026, October, https://doi.org/10.1007/s10670-026-01179-9, Boge.etal.2026a,
@Article{Boge.etal.2026a,
author={Boge, Florian J.; Krämer, Michael; Zeitnitz, Christian},
title={Deep {Learning} {Discovery} {Algorithms} in {High}-{Energy} {Physics} and the {Conflict} {Between} {Model}-{Independence} and {Robustness}},
journal={Erkenntnis},
month={October},
url={https://doi.org/10.1007/s10670-026-01179-9},
year={2026},
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...}}
