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Hey everyone,</div>
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At 1pm TODAY<b> please join us in L102</b> for a Cake Seminar by<b>
Zhite Yu</b>.</div>
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<div style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);" class="elementToProof">Zoom:
<a class="moz-txt-link-freetext" href="https://jlab-org.zoomgov.com/j/1611917868?pwd=xEbiVczPWIxYEntBlc0ofCEDOF3rkm.1">https://jlab-org.zoomgov.com/j/1611917868?pwd=xEbiVczPWIxYEntBlc0ofCEDOF3rkm.1</a></div>
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<div style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);" class="elementToProof"><b>Title:</b> Accessing x-dependent GPDs
from generative AI <b><br>
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</b></div>
<div style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);" class="elementToProof"><b>Abstract:</b><br>
Obtaining the x-dependent generalized parton distributions (GPDs)
is essential for advancing our understanding of hadron tomography.
However, this goal has been hindered by the limited sensitivity of
most well-known experimental processes, such as deeply virtual
Compton scattering (DVCS) and time-like Compton scattering (TCS).
In this talk, I will compare these traditional processes with new
ones that offer enhanced sensitivity to the x-dependence. By
employing a pixelated GPD construction using a normalizing flow
neural network, we can visualize and quantitatively examine the
point-by-point sensitivity encoded in the physical processes.</div>
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<div style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);" class="elementToProof">Best wishes,<br>
Adam, Joe, and Pia<br>
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