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<span style="color:rgb(0, 0, 0);font-family:Calibri, Arial, Helvetica, sans-serif;background-color:rgb(255, 255, 255);display:inline !important">Hello All,</span>
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Tomorrow, January 30th, at 1:00 PM (EST), Gurtej Kanwar will give our next (virtual) theory seminar of the new year.</div>
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Tej's seminar will be held virtually on our usual ZoomGov link:</div>
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<a href="https://jlab-org.zoomgov.com/j/1611179843?pwd=M09CNTFpbFVZSW1IQlhIMGp3RUVHUT09" id="LPNoLPOWALinkPreview" style="color:rgb(0, 120, 212)">https://jlab-org.zoomgov.com/j/1611179843?pwd=M09CNTFpbFVZSW1IQlhIMGp3RUVHUT09</a><br>
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Please see below for the title and abstract.</div>
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<u><b>Theory Seminar</b></u></div>
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Monday, January 30th at 1:00 PM</div>
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<u><b>Gurtej Kanwar</b></u><span> </span>(University of Bern)</div>
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will discuss "Tackling statistical noise in lattice calculations using complex contour deformations"</div>
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<u><b>Abstract:</b></u></div>
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Complex contour deformations of the path integral have previously been used to mitigate sign problems associated with non-zero chemical potential and real-time evolution in lattice field theories. This talk details their application to lattice calculations
where the vacuum path integral is instead real and positive -- allowing Monte Carlo sampling -- but observables are afflicted with a sign problem resulting in significant statistical noise. This is for example the case for many lattice calculations targeting
QCD phenomenology. In this context, contour deformations allow one to rewrite observables to minimize sign fluctuations while preserving their expectation value. We apply machine learning techniques to define and optimize families of contour deformations for
SU(N) variables and demonstrate exponential improvements in the signal-to-noise ratio of Wilson loops in proof-of-principle applications to U(1) and SU(N) lattice gauge theories.</div>
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See you tomorrow!</div>
<span style="color:rgb(0, 0, 0);font-family:Calibri, Arial, Helvetica, sans-serif;background-color:rgb(255, 255, 255);display:inline !important">Caroline, Colin & Patrick</span>
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