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pro vyhledávání: '"Ewen, Cedric"'
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditio
Externí odkaz:
http://arxiv.org/abs/2312.00123
Autor:
Buhmann, Erik, Ewen, Cedric, Kasieczka, Gregor, Mikuni, Vinicius, Nachman, Benjamin, Shih, David
Publikováno v:
Phys. Rev. D 109, 055015 (2024)
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for resonant anomaly detection have been developed, where sideband
Externí odkaz:
http://arxiv.org/abs/2310.06897
Autor:
Buhmann, Erik, Ewen, Cedric, Faroughy, Darius A., Golling, Tobias, Kasieczka, Gregor, Leigh, Matthew, Quétant, Guillaume, Raine, John Andrew, Sengupta, Debajyoti, Shih, David
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two novel methods that generate LHC jets as point clouds efficiently and accu
Externí odkaz:
http://arxiv.org/abs/2310.00049
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