Skip to main content

Research Repository

Advanced Search

Adversarially-trained autoencoders for robust unsupervised new physics searches

Blance, Andrew; Spannowsky, Michael; Waite, Philip

Adversarially-trained autoencoders for robust unsupervised new physics searches Thumbnail


Authors

Philip Waite



Abstract

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.

Citation

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. https://doi.org/10.1007/jhep10%282019%29047

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
DOI https://doi.org/10.1007/jhep10%282019%29047

Files

Published Journal Article (430 Kb)
PDF

Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

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





You might also like



Downloadable Citations