<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=Windows-1252">
<style type="text/css" style="display:none;"> P {margin-top:0;margin-bottom:0;} </style>
</head>
<body dir="ltr">
<div style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);" class="ContentPasted0">
Hello All,
<div><br class="ContentPasted0">
</div>
<div class="ContentPasted0 ContentPasted1 ContentPasted5">This coming Wednesday, November 9th at 1 pm (EST), Brandon Kriesten will give our next in-person cake seminar in CC L102. For those unable to attend in person, please join using the usual zoom link:
<a href="https://jlab-org.zoomgov.com/j/1611179843?pwd=M09CNTFpbFVZSW1IQlhIMGp3RUVHUT09" id="LPNoLPOWALinkPreview">
https://jlab-org.zoomgov.com/j/1611179843?pwd=M09CNTFpbFVZSW1IQlhIMGp3RUVHUT09</a><br>
</div>
<div class="_Entity _EType_OWALinkPreview _EId_OWALinkPreview _EReadonly_1"></div>
Please see below for the details.
<div><br class="ContentPasted0">
</div>
<div class="ContentPasted0"><u><b>Cake Seminar</b></u></div>
<div class="ContentPasted0">Wednesday, November 9th at 1:00 PM</div>
<div><br class="ContentPasted0">
</div>
<div class="ContentPasted0 ContentPasted2"><u><b>Brandon Kriesten</b></u> (Center for Nuclear Femtography)</div>
<div class="ContentPasted0 ContentPasted3">will discuss "Reconstructing lost information in deeply virtual exclusive processes"</div>
<div><br class="ContentPasted0">
</div>
<div class="ContentPasted0"><u><b>Abstract:</b></u></div>
<div class="ContentPasted0 ContentPasted4">It is known that deeply virtual exclusive reactions encode the dynamics of bound partons in nuclei through generalized parton distributions; however, there are many levels of abstraction in going from hadronic properties
to experimental observables. The EIC promises an unprecedented amount of data on exclusive reactions with high precision in a large range of kinematic settings as part of its 3D hadronic structure program. There is an immediate need to develop advanced theory
and computational tools in preparation for such a program. The FemtoNet framework was developed to answer this call, by reframing the analysis of exclusive experiments as a quantification of information loss and reconstruction. FemtoNet utilizes physics-informed
deep learning models to eliminate the “black box” nature of ML algorithms and re-inject the art of human intuition by imposing physics constraints at specific steps of the analysis. I will demonstrate what information physics-informed deep neural networks
are capable of in reconstructing from exclusive scattering experiments and give prospects for the future of such a program.<br>
</div>
<div><br class="ContentPasted0">
</div>
<div class="ContentPasted0">Best,</div>
Caroline, Colin, & Patrick</div>
</body>
</html>