Applications of Machine Learning in Lattice QCD

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• Bipasha Chakraborty
• Monday 20 July 2020, 17:00-17:30
• virtual ZOOM meeting ID: 263 591 6003, https://zoom.us/j/2635916003.

If you have a question about this talk, please contact Bingqing Cheng .

Lattice QCD is the only successful first principles method to deal with fundamental subatomic particles – quarks and gluons. In lattice QCD , we solve the theory of quarks and gluons, known as Quantum Chromodynamics (QCD), numerically using supercomputers. The results form crucial components of the worldwide particle physics research – in testing the Standard Model of particle physics, for finding new physics beyond it, to provide important information for ongoing and future experiments. However, lattice QCD calculations are computationally extremely challenging and expensive. Therefore, I have recently started an effort at DAMTP with my summer student to train a set of lattice QCD data using machine learning algorithms to generate more computationally challenging lattice QCD data sets. ML has not been so far explored much in lattice QCD , however, we drew some inspiration from the paper - https://ui.adsabs.harvard.edu/abs/2019PhRvD.100a4504Y/abstract.

In this talk, I will give a brief introduction to lattice QCD and application of ML in our data from lattice QCD . However, this work is in its infancy and we are looking for suggestions/collaborations.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• virtual ZOOM meeting ID: 263 591 6003, https://zoom.us/j/2635916003

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