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SUMMARY:Applications of Machine Learning in Lattice QCD - Bipasha Chakrabo
 rty
DTSTART:20200720T160000Z
DTEND:20200720T163000Z
UID:TALK150097@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Lattice QCD is the only successful first principles method to 
 deal with\nfundamental subatomic particles - quarks and gluons. In lattice
  QCD\, we\nsolve the theory of quarks and gluons\, known as Quantum Chromo
 dynamics\n(QCD)\, numerically using supercomputers. The results form cruci
 al\ncomponents of the worldwide particle physics research - in testing the
 \nStandard Model of particle physics\, for finding new physics beyond it\,
 \nto provide important information for ongoing and future experiments.\nHo
 wever\, lattice QCD calculations are computationally extremely\nchallengin
 g and expensive. Therefore\, I have recently started an effort\nat DAMTP w
 ith my summer student to train a set of lattice QCD data using\nmachine le
 arning algorithms to generate more computationally challenging\nlattice QC
 D data sets. ML has not been so far explored much in lattice\nQCD\, howeve
 r\, we  drew some inspiration from the paper -\nhttps://ui.adsabs.harvard.
 edu/abs/2019PhRvD.100a4504Y/abstract.\n\nIn this talk\, I will give a brie
 f introduction to lattice QCD and\napplication of ML in our data from latt
 ice QCD. However\, this work is in\nits infancy and we are looking for sug
 gestions/collaborations.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
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