2018/05/31 by Johann Brehmer, K. Cranmer, Kyle Cranmer +2 · 3 citations
Mathematics · Physics and Astronomy · #Artificial intelligence #Computer science #Dimension (graph theory) #Estimator #Function (biology) #Higgs boson #High-Energy Particle Collisions Research #Inference #Large Hadron Collider #Machine learning #Mathematics #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Physics beyond the Standard Model #Statistics #hep-ph #physics.data-an #stat.ML
paper · pdf · doi:10.1103/physrevd.98.052004
published as Phys. Rev. D 98, 052004 (2018) · See also the companion publication "Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00013, a brief introduction presenting the key ideas. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: Added references. v3: Improved description of algorithms, added references. v4: Clarified text, added references
arxiv created 2018/07/26 · openalex publication_date 2018/09/12 · arxiv updated 2018/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate the likelihood ratio. The new methods scale well to many observables and high-dimensional parameter spaces, do not require any approximations of the parton shower and detector response, and can be evaluated in microseconds. Using weak-boson-fusion Higgs production as an example process, we compare the performance of several techniques. The best results are found for likelihood ratio estimators trained with extra information about the score, the gradient of the log likelihood function with respect to the theory parameters. The score also provides sufficient statistics that contain all the information needed for inference in the neighborhood of the Standard Model. These methods enable us to put significantly stronger bounds on effective dimension-six operators than the traditional approach based on histograms. They also outperform generic machine learning methods that do not make use of the particle physics structure, demonstrating their potential to substantially improve the new physics reach of the Large Hadron Collider legacy results.