Wasserstein normalized autoencoder for anomaly detection
Yazarlar (150)
Roumyana Hadjiiska
Plamen Iaydjiev
Mariana Shopova
Georgi Sultanov
Anton Dimitrov
Leander Litov
Borislav Pavlov
Peicho Petkov
Anton Petrov
Sumit Keshri
David Nicolas Laroze Navarrete
Shalini Thakur
William Brooks
Tongguang Cheng
Tahir Javaid
Long Wang
Li Yuan
Zhen Hu
Zhengchen Liang
Jinfeng Liu
Sinop Üniversitesi
Sinop Üniversitesi
Aram Hayrapetyan
Vladimir Makarenko
Armen Tumasyan
Wolfgang Adam
Janik Walter Andrejkovic
Lisa Benato
Thomas Bergauer
Marko Dragicevic
Cristina Giordano
Priya Sajid Hussain
Manfred Jeitler
Natascha Krammer
Prof. Dr. Hasan OĞUL Sinop Üniversitesi, Türkiye
Dietrich Liko
Mark Matthewman
Ivan Mikulec
Jochen Schieck
Robert Schöfbeck
Dennis Schwarz
Maryam Shooshtari
Mangesh Sonawane
Wolfgang Waltenberger
Claudia-Elisabeth Wulz
Tahys Janssen
Hyejin Kwon
Daniel Ocampo Henao
Thomas van Laer
Pierre van Mechelen
Jas Bierkens
Nordin Breugelmans
Jorgen d'Hondt
Soumya Dansana
Alexandre de Moor
Martin Delcourt
Felix Heyen
Yanwen Hong
Pavlo Kashko
Steven Lowette
Inna Makarenko
Denise Müller
Juhee Song
Stefaan Tavernier
Michael Tytgat
Gerrit Patrick van Onsem
Senne van Putte
David Vannerom
Bugra Bilin
Barbara Clerbaux
Aloke Kumar Das
Isabelle de Bruyn
Gilles de Lentdecker
Hugues Evard
Laurent Favart
Paraskevas Gianneios
Ali Khalilzadeh
Fakhri Alam Khan
Andrea Malara
Muhammad Aamir Shahzad
Laurent Thomas
Max Vanden Bemden
Pascal Vanlaer
Fengwangdong Zhang
Maarten de Coen
Didar Dobur
Gul Gokbulut
Joscha Knolle
David Marckx
Kirill Skovpen
Niels van den Bossche
Jan van der Linden
Jules Vandenbroeck
Liam Wezenbeek
Samuel Bein
Anna Benecke
Agni Bethani
Giacomo Bruno
Alessandra Cappati
Jerome de Favereau de Jeneret
Christophe Delaere
Andrea Giammanco
Ahmet Oguz Guzel
Vincent Lemaitre
Jindrich Lidrych
Paul Malek
Paola Mastrapasqua
Semra Turkcapar
Gilvan Alves
Mapse Barroso Ferreira Filho
Eduardo Coelho
Carsten Hensel
Thales Menezes de Oliveira
Clemencia Mora Herrera
Patricia Rebello Teles
Mariana Soeiro
Antonio Vilela Pereira
Walter Luiz Aldá Júnior
Helena Brandao Malbouisson
Wagner Carvalho
Jose Chinellato
Matheus Costa Reis
Eliza Melo da Costa
Gustavo Gil da Silveira
Dilson de Jesus Damiao
Sandro Fonseca de Souza
Raphael Gomes de Souza
Silas Jesus
Tulio Laux Kuhn
Matheus Macedo
Kevin Mota Amarilo
Luiz Mundim
Helio Nogima
Joao Pedro Pinheiro
Alberto Santoro
Andre Sznajder
Mauricio Thiel
Felipe Torres da Silva de Araujo
Cesar Augusto Bernardes
Florian Damas
Thiago Tomei
Eduardo de Moraes Gregores
Bruno Lopes da Costa
Isabela Maietto Silverio
Pedro G Mercadante
Sergio F Novaes
Breno Orzari
Sandra Padula
Valerie Scheurer
Aleksandar Aleksandrov
Georgy Antchev
Petar Danev
| Makale Türü |
Özgün Makale
(Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale)
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| Dergi Adı | No journal information | ||
| Makale Dili | – | Basım Tarihi | 10-2025 |
| Makale Linki | https://hal.science/hal-05317231/ | ||
| UAK Araştırma Alanları |
Nükleer Fizik
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| Özet |
| A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution -- a Boltzmann distribution where the energy is the reconstruction error of the autoencoder -- and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets -- conical sprays of visible standard model particles and invisible dark matter states -- with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated standard model processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model consistently demonstrates stable, convergent training and achieves strong classification performance across a wide range of signals, improving upon standard normalized autoencoders, while remaining agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks. |
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