mlsanityData to 7 Oct 2026

Constraining Effective Field Theories with Machine Learning

arXiv 1805.00013 ↗First posted 30 Apr 2018hep-ph physics.data-an stat.MLcited by 26

Not on the map. Why?

Abstract (arXiv)

We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte-Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale well to processes with many observables and theory parameters, do not require any approximations of the parton shower or detector response, and can be evaluated in microseconds. We show that they allow us to put significantly stronger bounds on dimension-six operators than existing methods, demonstrating their potential to improve the precision of the LHC legacy constraints.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 4; 2023: 4; 2024: 2; 2025: 7; 2026: 1.

26 papers.

Sort:

By area:

  1. 2021Benchmarking Simulation-Based Inferencecited by 160
  2. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  3. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  4. 2024Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physicscited by 36
  5. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  6. 2021E Pluribus Unum Ex Machina: Learning from Many Collider Events at Oncecited by 15
  7. 2022Black-box Bayesian inference for economic agent-based modelscited by 13
  8. 2021A Cautionary Tale of Decorrelating Theory Uncertaintiescited by 11
  9. 2023Learning Likelihood Ratios with Neural Network Classifierscited by 11
  10. 2021Quantum-inspired event reconstruction with Tensor Networks: Matrix Product Statescited by 9
  11. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  12. 2022Contrastive Neural Ratio Estimation for Simulation-based Inferencecited by 7
14 more
  1. 2023ELSA -- Enhanced latent spaces for improved collider simulationscited by 5
  2. 2026Scaling laws for amplitude surrogatescited by 5
  3. 2021Inferring dark matter substructure with astrometric lensing beyond the power spectrumcited by 4
  4. 2022Exploration of Parameter Spaces Assisted by Machine Learningcited by 3
  5. 2025ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Datasetcited by 3
  6. 2024FAIR Universe HiggsML Uncertainty Dataset and Competitioncited by 2
  7. 2025Data-Driven High-Dimensional Statistical Inference with Generative Modelscited by 2
  8. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  9. 2021Cost-based feature selection for network model choicecited by 1
  10. 2025Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformerscited by 1
  11. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  12. 2023Designing Observables for Measurements with Deep Learningcited by 0
  13. 2025Strengthening Anomaly Awarenesscited by 0
  14. 2025Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flowscited by 0