2018/05/31 by Johann Brehmer, Kyle Cranmer, Gilles Louppe +1 · 3 citations
Mathematics · Physics and Astronomy · #hep-ph #physics.data-an #stat.ML
paper · pdf · doi:10.1103/physrevlett.121.111801
published as Phys. Rev. Lett. 121, 111801 (2018) · See also the companion publication "A Guide to Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00020, an in-depth analysis of machine learning techniques for LHC measurements. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: New schematic figure explaining the new algorithms, added references. v3, v4: Added references
arxiv created 2018/07/26 · arxiv updated 2018/09/19
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.