<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div style="margin-top: 0px; margin-bottom: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 16px;" class=""><span style="font-size: 10pt; white-space: pre-wrap;" class="">Dear all,</span><br class=""></div><div id="Signature" class=""><div id="divtagdefaultwrapper" dir="ltr" class=""><pre cols="72" style="font-family: Calibri, Helvetica, sans-serif; font-size: 16px; white-space: pre-wrap;" class=""><p style="margin-top: 0px; margin-bottom: 0px; font-family: Calibri, Helvetica, sans-serif;" class=""></p><div style="margin-top: 0px; margin-bottom: 0px; font-family: Calibri, Helvetica, sans-serif, serif, EmojiFont;" class=""><span style="font-size: 10pt; font-family: Arial, Helvetica, sans-serif, serif, EmojiFont;" class=""><span style="font-family: Calibri, Helvetica, sans-serif;" class="">Here is a reminder of the</span><span style="font-family: Calibri, Helvetica, sans-serif;" class=""> theory</span></span><span style="font-size: 10pt; font-family: Arial, Helvetica, sans-serif, serif, EmojiFont;" class=""><span style="font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;" class=""> </span><span class="m_4491268831180820737il" style="font-size: 10pt;"><span class="il" style="font-family: Calibri, Helvetica, sans-serif;">seminar tomorrow</span></span><span style="font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;" class="">:</span></span></div><div style="margin-top: 0px; margin-bottom: 0px; font-family: Calibri, Helvetica, sans-serif, serif, EmojiFont;" class=""><span style="font-size: 10pt; font-family: Arial, Helvetica, sans-serif, serif, EmojiFont;" class=""><span style="font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;" class=""><br class=""></span></span></div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="font-family: Arial, Helvetica, sans-serif, serif, EmojiFont; font-size: 11pt;" class=""><span style="font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;" class="">Monday, January</span></span><span id="m_4491268831180820737m_-8787377761327560462x_OBJ_PREFIX_DWT98_com_zimbra_date" style="font-family: monospace, serif, EmojiFont; font-size: 14.16px; color: rgb(0, 90, 149);" class=""><span id="m_4491268831180820737m_-8787377761327560462x_OBJ_PREFIX_DWT99_com_zimbra_date" style="color: rgb(0, 0, 0); font-size: 10pt; font-family: Calibri, Helvetica, sans-serif;" class=""> 28th, 1:00PM</span></span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 10pt;" class=""><span style="font-size: 10pt;" class="">, Room L102</span></span></div><pre cols="72" style="white-space: pre-wrap;" class=""><div style="margin-top: 0px; margin-bottom: 0px;" class=""><font face="Calibri, Helvetica, sans-serif" class=""><span style="font-size: 10pt;" class=""><span style="font-size: 10pt;" class="">Boram Yoon (Los Alamos),"</span><span style="font-size: 10pt;" class="">Estimation of the Lattice QCD Observables using Machine Learning</span><span style="font-size: 10pt;" class="">"</span><span style="font-size: 10pt;" class="">
</span></span></font></div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><font face="Calibri, Helvetica, sans-serif" class=""><span style="font-size: 10pt;" class=""><span style="color: rgb(33, 33, 33); font-size: 10pt;" class=""><span style="color: rgb(0, 0, 0);" class="">BlueJeans connection</span>: </span><a href="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" originalsrc="https://bluejeans.com/321085255" shash="VeUfdIF/eVtRJvsRnQSPGrYM3m2w5sYqUXq0hu3Qq2kYROSdvvfqqIBSdAb3jbBuawEA2gnu8XBXg1eTF9U1OAqHspEe2sOhRgZwdTYMe9YHWRKUeV144+8aZ2kM/VAPo7r5zsxJcT9LEggyxD1GN60eSr+KwtHz1WDTauFz3TI=" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" id="LPlnk796215" class="OWAAutoLink" previewremoved="true" style="font-family: wf_segoe-ui_normal, "Segoe UI", "Segoe WP", Tahoma, Arial, sans-serif, serif, EmojiFont; font-size: 14.6667px;"><span style="color: rgb(0, 111, 201); font-family: Calibri, Helvetica, sans-serif; font-size: 10pt;" class="">https://bluejeans.com/321085255</span></a><br class=""></span></font></div></pre><font face="Calibri, Helvetica, sans-serif" class=""><span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;" class=""><div style="margin-top: 0px; margin-bottom: 0px; color: rgb(0, 0, 0);" class=""><font face="Calibri, Helvetica, sans-serif" class=""><span style="font-size: 13.3333px;" class=""><span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;" class=""><u class=""></u><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 13.3333px;" class=""><u class=""><span style="color: rgb(0, 0, 0);" class="">Abstract:</span></u></span><br class=""></span></span></font></div><span style="color: rgb(0, 0, 0);" class="">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.</span></span></font></pre><pre cols="72" style="white-space: pre-wrap;" class=""><font face="Arial, Helvetica, sans-serif" size="2" class="">Thank you.</font></pre><pre cols="72" style="white-space: pre-wrap;" class=""><font face="Arial, Helvetica, sans-serif" size="2" class="">-Carlota, Vincent, Raza</font></pre></div></div><div class=""><br class=""></div></body></html>