mlsanityData to 14 Oct 2026

Scaling laws for amplitude surrogates

arXiv 2601.13308 ↗First posted 19 Jan 2026hep-ph cs.LGcited by 6

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Abstract (arXiv)

Scaling laws describing the dependence of neural network performance on the amount of training data, the spent compute, and the network size have emerged across a huge variety of machine learning task and datasets. In this work, we systematically investigate these scaling laws in the context of amplitude surrogates for particle physics. We show that the scaling coefficients are connected to the number of external particles of the process. Our results demonstrate that scaling laws are a useful tool to achieve desired precision targets.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 65 of its 126 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

65 of 126 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Neural Scaling Laws for Boosted Jet Taggingcited by 12
  2. 2026Towards Engineering Scaling Laws with Pretraining Data Compositioncited by 2
  3. 2026Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Taskscited by 2
  4. 2026Event Generation with Parallel Langevin Sampling and Learned Stein Diagnosticscited by 0
  5. 2026Machine learning for the LHC physics program: a 2025-2026 stocktakecited by 0
  6. 2026How to scale your HEP ML models: A recipe for robust architecture comparisons at scalecited by 0