arXiv Open Access 2023

The Mass-ive Issue: Anomaly Detection in Jet Physics

Tobias Golling Takuya Nobe Dimitrios Proios John Andrew Raine Debajyoti Sengupta +6 lainnya

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In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches. In this paper we highlight the main challenge of applying anomaly detection to jet physics, where preserving an unbiased estimator of the jet mass remains a critical piece of any model independent search. Using Variational Autoencoders and multiple industry-standard anomaly detection metrics, we demonstrate the unavoidable nature of this problem.

Topik & Kata Kunci

hep-ph

Penulis (11)

Tobias Golling

Takuya Nobe

Dimitrios Proios

John Andrew Raine

Debajyoti Sengupta

Slava Voloshynovskiy

Jean-Francois Arguin

Julien Leissner Martin

Jacinthe Pilette

Debottam Bakshi Gupta

Amir Farbin

Format Sitasi

APA MLA BibTeX

Golling, T., Nobe, T., Proios, D., Raine, J.A., Sengupta, D., Voloshynovskiy, S. et al. (2023). The Mass-ive Issue: Anomaly Detection in Jet Physics. https://arxiv.org/abs/2303.14134

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓