Reference : Constraining Effective Field Theories with Machine Learning
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
Engineering, computing & technology : Computer science
Constraining Effective Field Theories with Machine Learning
Brehmer, Johann [> >]
Cranmer, Kyle [> >]
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Pavez, Juan [> >]
Physical Review Letters
American Physical Society
Yes (verified by ORBi)
New York
[en] High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability ; Statistics - Machine Learning
[en] 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.
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 . v2: New schematic figure explaining the new algorithms, added references. v3: Added references

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