mlsanityData to 7 Oct 2026

Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production

arXiv 2405.15847 ↗First posted 24 May 2024hep-ph stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 110 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

9 of 110 references.

References · 9

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019The frontier of simulation-based inferencecited by 490
  4. 2015Approximating Likelihood Ratios with Calibrated Discriminative Classifierscited by 87
  5. 2016Parameterized Machine Learning for High-Energy Physicscited by 33
  6. 2019MadMiner: Machine learning-based inference for particle physicscited by 32
  7. 2023Learning Likelihood Ratios with Neural Network Classifierscited by 11
  8. 2022Differentiable Matrix Elements with MadJaxcited by 8
  9. 2022New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimationcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Data-Driven High-Dimensional Statistical Inference with Generative Modelscited by 2
  2. 2025Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs productioncited by 2
  3. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  4. 2025One-shot Conditional Sampling: MMD meets Nearest Neighborscited by 0