mlsanityData to 7 Oct 2026

MadMiner: Machine learning-based inference for particle physics

arXiv 1907.10621 ↗First posted 24 Jul 2019hep-ph hep-ex physics.data-ancited by 32

Not on the map. Why?

Abstract (arXiv)

Precision measurements at the LHC often require analyzing high-dimensional event data for subtle kinematic signatures, which is challenging for established analysis methods. Recently, a powerful family of multivariate inference techniques that leverage both matrix element information and machine learning has been developed. This approach neither requires the reduction of high-dimensional data to summary statistics nor any simplifications to the underlying physics or detector response. In this paper we introduce MadMiner, a Python module that streamlines the steps involved in this procedure. Wrapping around MadGraph5_aMC and Pythia 8, it supports almost any physics process and model. To aid phenomenological studies, the tool also wraps around Delphes 3, though it is extendable to a full Geant4-based detector simulation. We demonstrate the use of MadMiner in an example analysis of dimension-six operators in ttH production, finding that the new techniques substantially increase the sensitivity to new physics.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 4; 2023: 5; 2024: 4; 2025: 6; 2026: 4.

32 papers.

Sort:

By area:

  1. 2021An Introduction to Deep Generative Modelingcited by 104
  2. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  3. 2023KL Convergence Guarantees for Score diffusion models under minimal data assumptionscited by 61
  4. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  5. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  6. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 45
  7. 2022Taming Hyperparameter Tuning in Continuous Normalizing Flows Using the JKO Schemecited by 17
  8. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  9. 2023Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inferencecited by 14
  10. 2021A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excesscited by 12
  11. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  12. 2023Learning Likelihood Ratios with Neural Network Classifierscited by 11
20 more
  1. 2021Quantum-inspired event reconstruction with Tensor Networks: Matrix Product Statescited by 9
  2. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  3. 2024Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoodscited by 5
  4. 2026Scaling laws for amplitude surrogatescited by 5
  5. 2024Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Productioncited by 4
  6. 2021F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuitscited by 3
  7. 2023Generative Invertible Quantum Neural Networkscited by 3
  8. 2022New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimationcited by 2
  9. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  10. 2025Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs productioncited by 2
  11. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  12. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  13. 2026Gradient estimators for parameter inference in discrete stochastic kinetic modelscited by 1
  14. 2021Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciencescited by 0
  15. 2023Designing Observables for Measurements with Deep Learningcited by 0
  16. 2024Modeling Sampling Distributions of Test Statistics with Autogradcited by 0
  17. 2025Communicating Likelihoods with Normalising Flowscited by 0
  18. 2025What is AI, what is it not, how we use it in physics and how it impacts... youcited by 0
  19. 2025Increasing Information Extraction in Low-Signal Regimes via Multiple Instance Learningcited by 0
  20. 2026Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarioscited by 0