"Constraining Effective Field Theories with Machine Learning"
Presented by Johann Brehmer, New York University
Wednesday, June 27, 2018, 2:00 pm — Small Seminar Room, Bldg. 510
An important part of the LHC legacy will be precise limits on indirect effects of new physics, parameterized for instance in an Effective Field Theory (EFT). But measuring these parameters in complex processes is often challenging for established analysis methods. We present powerful new inference techniques based on machine learning. They scale well to complicated problems with many parameters and observables and do not require any approximations on the parton shower or detector effects. In an example analysis of WBF Higgs production we show that they enable us to put stronger bounds on EFT parameters than established methods, demonstrating their potential to improve the new physics reach of the LHC legacy results. We also comment on the application of these new "likelihood-free" or "simulator-based" inference techniques to a broad class of problems outside of particle physics, for instance in cosmology, epidemiology, and genetics.
Hosted by: Christopher Murphy
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