[Theory-seminars] Cake Seminar tomorrow

[Joseph Karpie]

Hey everyone,

We will be having a double feature Cake Seminar tomorrow. At 1pm we will be hearing talks from Nick Chambers and Yamil Medrano Cahuana. Please join us in F224-225.

Nick Chambers:
Title: Progress in Three-Body Partial-Wave Formalism

Abstract: Among the many challenges in predicting properties of hadrons from QCD is the modeling of interactions involving three or more particles. Three body effects are critical to studies of exotic hadrons like the Tcc and benchmark resonances like the ω, which have been observed to decay into three-body final states. Resonances like the ω will appear in partial wave projections of 3π→3π scattering amplitudes, but recent descriptions of these amplitudes from S-matrix theory are incomplete and rely on an asymmetric choice of basis. I will present a symmetrized form of a 3→3 reaction amplitude that has this unphysical basis dependence removed, and I will demonstrate the validity of our result with a numerical application of our symmetrization procedure to a toy model three-pion process. We visualize this system with Dalitz plots that exhibit the rich symmetry features which are anticipated of three-pion interactions. The Dalitz plots we present are one example of an  observable that could be constructed using our symmetrized amplitude to predict features of hadronic interactions from QCD.


Yamil Medrano Cahuana:
Title: Gaussian Processes for Inferring Parton Distributions

Abstract: I will present an overview of my work on solving the inverse problem of reconstructing parton distribution functions from Lattice QCD data within the pseudo-PDF framework. I will outline the links between parametric and non-parametric strategies and explain how Gaussian processes provide flexible Bayesian priors that encode correlations and physical constraints without fixing a functional shape. To evaluate how strongly the data constrain the reconstruction, we quantify information gain through the Kullback–Leibler divergence. Tests on synthetic data confirm the consistency and robustness of the method. These results support Gaussian-process regression as a systematic non-parametric approach to PDF reconstruction, offering controlled uncertainties and reduced model bias in Lattice QCD analyses.

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Joe Karpie
Pronouns: He/His/Him
Postdoctoral Fellow
Theoretical and Computational Physics Center
Jefferson Lab

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