Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/74961
Título: | Finding new physics without learning about it: anomaly detection as a tool for searches at colliders |
Autor(es): | Crispim Romão, M. Castro, Nuno Filipe Pedro, R. |
Data: | 15-Jan-2021 |
Editora: | Springer |
Revista: | European Physical Journal C |
Citação: | Crispim Romão, M., Castro, N.F. & Pedro, R. Finding new physics without learning about it: anomaly detection as a tool for searches at colliders. Eur. Phys. J. C 81, 27 (2021). https://doi.org/10.1140/epjc/s10052-020-08807-w |
Resumo(s): | In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram-Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders' data. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/74961 |
DOI: | 10.1140/epjc/s10052-020-08807-w |
ISSN: | 1434-6044 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | LIP - Artigos/papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Crispim_Romao_2021_Article_Finding_New_Physics_Without_Learning.pdf | 6,76 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons