<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class="">Hi All,<div class=""><br class=""></div><div class=""> As I mentioned at last week’s meeting, I’ve been invited to give a talk at the </div><div class="">DNP meeting in Pittsburg on how we’ve used Machine Learning in the </div><div class="">development of the !muon detector. Below is a draft of the abstract I plan</div><div class="">to submit. Comments are welcomed. </div><div class=""><br class=""></div><div class="">Regards,</div><div class="">-David</div><div class=""><br class=""></div><div class=""><br class=""></div><div class=""><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">The Charged Pion Polarizability(CPP) experiment is approved </span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">to run in Hall-D at Jefferson Lab using the GlueX detector. </span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">CPP requires that $\pi^{+{}} \pi^{-{}}$ production events be </span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">distinguished from $\mu^{+{}}\mu^{-{}}$ to better than 99\% accuracy. </span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">This drives the design of a new MWPC-based detector capable of </span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">separating the $\pi$ events from the $\mu$ events. A multivariate</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">analysis of simulated data was initially done to study the</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">feasibility of a detector with this level of performance. More</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">recently, the design parameters of the detector have been</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">refined using a similar technique. Details on the initial study</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">and how machine learning has contributed to the detector design</span></div><div style="margin: 0px; font-size: 11px; line-height: normal; font-family: Menlo;" class=""><span style="font-variant-ligatures: no-common-ligatures" class="">will be presented.</span></div></div></body></html>