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Author van Beekveld, M.; Caron, S.; Hendriks, L.; Jackson, P.; Leinweber, A.; Otten, S.; Patrick, R.; Ruiz de Austri, R.; Santoni, M.; White, M.
Title Combining outlier analysis algorithms to identify new physics at the LHC Type Journal Article
Year 2021 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 09 Issue 9 Pages 024 - 33pp
Keywords Phenomenological Models; Supersymmetry Phenomenology
Abstract The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a beta-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
Address [van Beekveld, Melissa] Clarendon Lab, Rudolf Peierls Ctr Theoret Phys, 20 Pks Rd, Oxford OX1 3PU, England, Email: mcbeekveld@gmail.com;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1029-8479 ISBN (up) Medium
Area Expedition Conference
Notes WOS:000695421600003 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4973
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