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Similar

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Original
2009
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Then

A. De Simone and T. Jacques, Guiding New Physics Searches with Unsupervised Learning, Eur. Phys. J. C79
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2019
Later among the works it cites.
G. M. Alessandro Casa, Nonparametric semisupervised classification for signal detection in high energy physics, (2019), arXiv:1809.02977 [hep-ex]
Original
2019
Later among the works it cites.
R. T. D’Agnolo and A. Wulzer, Learning New Physics from a Machine, Phys. Rev. D99
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2019
Later among the works it cites.
L.-G. Xia, QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics, Nucl. Instrum. Meth. A930
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2019
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2021

Online-compatible Unsupervised Non-resonant Anomaly Detection

Mikuni, Vinicius, Nachman, Benjamin, Shih, David

Understand

There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner.

Most proposals for new methods focus exclusively on signal sensitivity.
However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events.
We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation.