REVIEW 4 cited by
Differentiable Matrix Elements with MadJax
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 4 Pith papers
-
Generative Amplification with Surrogate Monte Carlo
An amplitude surrogate trained on a few thousand exact LHC amplitude points statistically outperforms the training data, with largest amplification in sparsely populated kinematic tails of Z+g and Z+4g production.
-
Automated NRQCD and NRQED simulations of quarkonium and leptonium production with P-wave states and physical-mass effects
MadSONS extends MadGraph to automated LO NRQCD/NRQED event generation for arbitrary S- and P-wave bound states, with dual-number projectors and physical-mass reshuffling.
-
MadSpace -- Event Generation for the Era of GPUs and ML
MadSpace is a GPU-native compute-graph event-generation library that matches MadGraph LO physics in validation and introduces the analytic FastRambo phase-space mapping.
-
Variational Inference Using a Differentiable Multigrid Linear Solver
A hand-coded adjoint multigrid solver, wrapped in JAX, enables memory-efficient variational inference for a 3D tissue-imaging inverse problem.
Discussion (0). Continue with ORCID to comment.