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S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri, and R. Verheyen, Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
Original
1901
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B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
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A. Butter et al
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1902
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H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
Original
1902
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A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
Original
1907
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M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
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1912
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A. Ghosh, X. Ju, B. Nachman, and A. Siodmok, Towards a deep learning model for hadronization
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2024

A Lorentz-Equivariant Transformer for All of the LHC

Brehmer, Johann, Bresó, Víctor, de Haan, Pim et al.

Understand

We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider.

L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations.
The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed.
We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network.