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Searching for New Physics with Deep Autoencoders · Around

2018

Searching for New Physics with Deep Autoencoders

Farina, Marco, Nakai, Yuichiro, Shih, David

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We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning.

The key idea of the autoencoder is that it learns to map "normal" events back to themselves, but fails to reconstruct "anomalous" events that it has never encountered before.
The reconstruction error can then be used as an anomaly threshold.
We demonstrate the effectiveness of this idea using QCD jets as background and boosted top jets and RPV gluino jets as signal.

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