Combining outlier analysis algorithms to identify new physics at the LHC.

Autor:	van Beekveld, Melissa, Caron, Sascha, Hendriks, Luc, Jackson, Paul, Leinweber, Adam, Otten, Sydney, Patrick, Riley, de Austri, Roberto Ruiz, Santoni, Marco, White, Martin
Předmět:	GAUSSIAN mixture models LARGE Hadron Collider ALGORITHMS RANDOM forest algorithms LATENT variables EXPECTATION-maximization algorithms
Zdroj:	Journal of High Energy Physics; Sep2021, Vol. 2021 Issue 9, p1-33, 33p
Abstrakt:	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 β-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 super- symmetric 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. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink