Unbinned and High-dimensional Unfolding with Machine Learning

by Ben Nachman (Lawrence Berkeley National Lab. (US))


Abstract: Machine learning (ML) tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before.  In this talk, I will introduce new methods for ML-based unfolding/deconvolution, discuss public results using these methodologies, and end with challenges for the future.

Ben Nachman is the group leader of the Lawrence Berkeley's "Machine Learning  for fundamental Physics" and is a member of the ATLAS Collaboration. He is a leading figure in Machine Learning for Particle Physics, and a much sought after speaker for Seminars. His awards include the European Physical Society's Young Physicists Prize, and he has funding from the Department of Energy's Early Career Research Programme.


Organized by

O. Behnke, L. Lyons, L. Moneta, N. Wardle

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Olaf Behnke
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Nicholas Wardle
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