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Statistical aspects in the determination of the proton's structure

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If you have a question about this talk, please contact Dr Priscilla Canizares .

The interpretation of the high energy physics data taken at the Large Hadron Collider (LHC), and the assessment of possible hints of physics beyond the Standard Model (SM) of particle physics, require the precise knowledge of the proton structure in terms of a set of functions parametrizing its content in terms of its elementary constituents, quarks and gluons. These functions are known as Parton Distribution Functions (PDFs). In this talk I will illustrate the statistical aspects involved in the solution of the inverse problem of determining PDFs from the LHC data, and how machine learning combined with Bayesian sampling techniques can be applied to obtain a faithful and robust determination of such functions.

This talk is part of the Astro Data Science Discussion Group series.

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