Presenter: Francesca Capel, MPP, ODSL
Title: A Bayesian approach to parton density extraction
Hadrons are made up of quarks held together by the strong force. The valence quarks that define these hadrons exist in a sea of virtual quarks and gluons, and this structure is described by the parton distribution functions (PDFs). These PDFs are of fundamental importance to our understanding of QCD and can be extracted from accelerator measurements in which hadrons are probed through collisions with electrons and positrons.We present a new approach to extracting PDFs based on a full forward model from the input PDFs to the expected number of events in measurement bins. This model is then fit using Markov Chain Monte Carlo to infer the PDF parameters from data. Our approach is particularly interesting for the “high-x” data from the ZEUS experiment due to the low event numbers, where the assumptions used in existing methods break down. We present the techniques developed as well as their performance on simulated datasets.
As usual, the format will be a short presentation followed by plenty of discussion.
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