Differentiable Matrix Elements with MadJax
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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
- 2019The frontier of simulation-based inferencecited by 490
- 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
- 2018Mining gold from implicit models to improve likelihood-free inferencecited by 60
- 2020TorchMD: A deep learning framework for molecular simulationscited by 55
- 2020PDFFlow: parton distribution functions on GPUcited by 5
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 0; 2026: 2.
8 papers.
- 2024Finetuning Foundation Models for Joint Analysis Optimizationcited by 18
- 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
- 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physicscited by 6
- 2024Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Productioncited by 4
- 2023Differentiable Vertex Fitting for Jet Flavour Taggingcited by 3
- 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Designcited by 1
- 2024Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physicscited by 0
- 2026Event Generation with Parallel Langevin Sampling and Learned Stein Diagnosticscited by 0
- 2026Event Generation with Parallel Langevin Sampling and Learned Stein Diagnostics12 Jun 2026
- 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design6 Feb 2026
- 2024Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics25 Nov 2024
- 2024Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production24 May 2024
- 2024Finetuning Foundation Models for Joint Analysis Optimization24 Jan 2024
- 2023Differentiable Vertex Fitting for Jet Flavour Tagging19 Oct 2023
- 2023Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics31 Aug 2023
- 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulation15 Mar 2022