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2021

Autoencoders for unsupervised anomaly detection in high energy physics

Finke, Thorben, Krämer, Michael, Morandini, Alessandro et al.

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Autoencoders are widely used in machine learning applications, in particular for anomaly detection.

Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches.
We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations.
As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images.

Autoencoders for unsupervised anomaly detection in high energy physics · Around

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