Anomaly Detection for Physics Analysis and Less than Supervised Learning
Abstract
Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning directly from data. There has been a significant growth in new ideas and they are just starting to be applied to experimental data. This chapter introduces these new anomaly detection methods, which range from fully supervised algorithms to unsupervised, and include weakly supervised methods.
- Publication:
-
arXiv e-prints
- Pub Date:
- October 2020
- DOI:
- 10.48550/arXiv.2010.14554
- arXiv:
- arXiv:2010.14554
- Bibcode:
- 2020arXiv201014554N
- Keywords:
-
- High Energy Physics - Phenomenology;
- High Energy Physics - Experiment;
- Physics - Data Analysis;
- Statistics and Probability
- E-Print:
- 22 pages, 7 figures. To appear in "Artificial Intelligence for Particle Physics", World Scientific Publishing Co