Unsupervised and lightly supervised learning in particle physics

Abstract

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.

41 pages, 18 figures, 2 tables. Matches the published version

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Cited by in corpus (3)

• Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
• Inferring correlated distributions: boosted top jets
• Shedding Light on Dark Matter at the LHC with Machine Learning