Mar 23 – 27, 2020
Brooklyn, NY
US/Eastern timezone

Solving the inverse problem for nucleon structure through machine learning

Mar 24, 2020, 9:30 AM
20m
Brooklyn, NY

Brooklyn, NY

333 Adams Street, Brooklyn, New York 11201, USA
Contributed Talk Spin Physics Spin Physics

Speaker

nobuo sato (Jefferson Lab)

Description

We present a new approach to performing Bayesian inference for QCD analysis of nucleon structure and hadronization, using machine learning to construct the inverse function mapping quantum correlation functions to observables. The new concept provides an alternative paradigm to the standard maximum likelihood or Bayesian posterior sampling methods. The effectiveness of the new technology is illustrated with application to the extraction of parton distribution functions from deep-inelastic scattering data, with results compared to recent global QCD analyses.

Primary author

nobuo sato (Jefferson Lab)

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