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Adversarially-trained autoencoders for robust unsupervised new physics searches

Blance, Andrew; Spannowsky, Michael; Waite, Philip

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Philip Waite


Machine learning techniques in particle physics are most powerful when they are trained directly on data, to avoid sensitivity to theoretical uncertainties or an underlying bias on the expected signal. To be able to train on data in searches for new physics, anomaly detection methods are imperative, which can be realised by an autoencoder acting as an unsupervised classifier. The last source of uncertainties affecting the classifier are then experimental uncertainties in the reconstruction of the final-state objects. To mitigate their effect on the classifier and to allow for a realistic assessment of the method, we propose to combine the autoencoder with an adversarial neural network to remove its sensitivity to the smearing of the final-state objects. We quantify its effect and show that one can achieve a robust anomaly detection in resonance-induced tt¯ final states.


Blance, A., Spannowsky, M., & Waite, P. (2019). Adversarially-trained autoencoders for robust unsupervised new physics searches. Journal of High Energy Physics, 2019(10), Article 047.

Journal Article Type Article
Acceptance Date Sep 20, 2019
Online Publication Date Oct 4, 2019
Publication Date Jan 1, 2019
Deposit Date Oct 18, 2019
Publicly Available Date Oct 18, 2019
Journal Journal of High Energy Physics
Print ISSN 1126-6708
Publisher Scuola Internazionale Superiore di Studi Avanzati (SISSA)
Peer Reviewed Peer Reviewed
Volume 2019
Issue 10
Article Number 047


Published Journal Article (430 Kb)

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Copyright Statement
Open Access. This article is distributed under the terms of the Creative Commons<br /> Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in<br /> any medium, provided the original author(s) and source are credited.

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