https://doi.org/10.1140/epjs/s11734-024-01235-x
Review
Unsupervised and lightly supervised learning in particle physics
1
Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, 500 032, Hyderabad, Telangana, India
2
School of Physics, Indian Institute of Science Education and Research, 695 551, Thiruvananthapuram, Kerala, India
3
Center for Quantum Science and Technology, International Institute of Information Technology, 500 032, Hyderabad, Telangana, India
4
iHub-Data, International Institute of Information Technology, 500 032, Hyderabad, Telangana, India
e
monalisa.p@ihub-data.iiit.ac.in
Received:
30
March
2024
Accepted:
28
June
2024
Published online:
8
July
2024
We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
