[Theory-seminars] Theory Seminar

[Raza Sufian]

Dear all,
Here is a reminder of the theory seminar tomorrow:

Monday, January 28th, 1:00PM, Room L102
Boram Yoon (Los Alamos),"Estimation of the Lattice QCD Observables using Machine Learning"
BlueJeans connection: https://bluejeans.com/321085255 <https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbluejeans.com%2F321085255&data=02%7C01%7Csufian%40jlab.org%7Cd3db2408312946f3198b08d6831244df%7Cb4d7ee1f4fb34f0690372b5b522042ab%7C1%7C0%7C636840511829116964&sdata=ecAXmfIKCGG2dDrAAEMDt7cB9g3ieuIvCCE%2Bmo0VJ0g%3D&reserved=0>
Abstract:
I will present a novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice QCD observables. Lattice QCD, a non-perturbative formulation of QCD on a hypercubic Euclidean space-time grid, is solved using Markov chain Monte Carlo simulations. In the lattice QCD simulations, multiple observables are simultaneously measured on a series of Monte-Carlo samples of gluon fields as calculate expectation value, and the fluctuations of these observables over the Monte-Carlo samples are correlated. By exploiting the correlations between observables, one can build a machine learning algorithm that predicts values of a set of observables using the information on other observables. After predictions are made, the bias introduced by the prediction error is corrected and converted to the statistical uncertainty. The approach is demonstrated for two different lattice QCD calculations using the Boosted decision tree (BDT) regression ML algorithm: (1) prediction of the nucleon three-point correlation functions from the two-point correlation functions, and (2) prediction of charge-parity violating (CPV) phase of the neutron state for the quark chromo-electric dipole moment interactions from the regular two-point correlation functions calculated without CPV interactions.
Thank you.
-Carlota, Vincent, Raza

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