Abstract.
The interpretation of LHC data, and the assessment of possible hints of new physics, require the precise knowledge of the proton structure in terms of parton distribution functions (PDFs). In this talk I will illustrate how machine learning can be applied to obtain a faithful and robust determination of such functions. I will discuss recent progress in global fits of PDFs, and how to deal with data inconsistencies that might hamper the accuracy of the fit.
I will also present new studies that shed light on the interplay between the parametrization of the proton structure and the parametrization of new physics, according to two complementary approaches. On the one hand, a new framework is introduced, which allows the determination of PDFs alongside the Wilson coefficients of a model-independent EFT to parametrize heavy new physics. On the other hand, a systematic methodology designed to determine whether and how global PDF fits might inadvertently ‘fit away’ signs of new physics in the high-energy tails of the distributions are presented.
(The slides will be available here)