“Machine Learning for High Energy Physics, on and off the Lattice”

23 – 27 March

Organizers:

Andreas ATHENODOROU (Università di Pisa/I),
Dimitrios GIATAGANAS (National and Kapodistrian University, Athens/GR),
Biagio LUCINI (Swansea University, Swansea/UK),
Enrico RINALDI (Arithmer Inc., Tokyo/JG),
Kyle CRANMER (New York University, New York/US),
Costantia ALEXANDROU (The Cyprus Institute, University of Cyprus/CY)

Abstract

Machine learning (ML) has been recently used as a very effective tool for the study and prediction of data in various fields of physics, from statistical physics to theoretical high energy physics. The aim of this workshop is to bring together active researchers on ML and Physics to interact and initiate a collaborative effort to investigate timely problems on Lattices and Theoretical High Energy Physics. Hence we invite scientists with research areas covering a broad spectrum to present their work. Some of the topics which will be highlighted are supervised and unsupervised identification of phase transitions on lattice models, applications of generative algorithms in the production of lattice configurations, applications of machine learning estimators in observables in Lattice QCD and the connection of ML with Renormalization Group as well as the gauge/gravity correspondence.

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