mlsanityData to 7 Oct 2026

Differentiable Matrix Elements with MadJax

arXiv 2203.00057 ↗First posted 28 Feb 2022hep-ph cs.LG physics.comp-phcited by 8

Not on the map. Why?

Abstract (arXiv)

MadJax is a tool for generating and evaluating differentiable matrix elements of high energy scattering processes. As such, it is a step towards a differentiable programming paradigm in high energy physics that facilitates the incorporation of high energy physics domain knowledge, encoded in simulation software, into gradient based learning and optimization pipelines. MadJax comprises two components: (a) a plugin to the general purpose matrix element generator MadGraph that integrates matrix element and phase space sampling code with the JAX differentiable programming framework, and (b) a standalone wrapping API for accessing the matrix element code and its gradients, which are computed with automatic differentiation. The MadJax implementation and example applications of simulation based inference and normalizing flow based matrix element modeling, with capabilities enabled uniquely with differentiable matrix elements, are presented.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 26 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

5 of 26 references.

References · 5

  1. 2019The frontier of simulation-based inferencecited by 490
  2. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  3. 2018Mining gold from implicit models to improve likelihood-free inferencecited by 60
  4. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  5. 2020PDFFlow: parton distribution functions on GPUcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 0; 2026: 2.

8 papers.