Optimal equivariant architectures from the symmetries of matrix-element likelihoods
The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to evaluate the likelihood of observed events. In parallel, the advent of geometric deep learning has enabled neural networ...
| Published in: | Machine Learning: Science and Technology |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adbab1 |