Date:
-
Location:
CP 303
Speaker(s) / Presenter(s):
Dr. Brandon Kriesten, Argonne National Laboratory
Uncertainty quantification (UQ) plays a crucial role in the predictive power of nonperturbative quantum correlation functions at high precision. My research explores novel approaches to UQ in the context of parton distribution functions (PDFs), using uncertainty-aware machine learning techniques to map observables to underlying theoretical models and to navigate the complex parametric landscape of phenomenological scenarios, including the vast ecosystem of beyond-the-Standard-Model (BSM) configurations. By leveraging modern generative AI methods, I investigate how the inherent uncertainties in phenomenological extractions of collinear PDFs impact the landscape of potential New Physics models. My approach integrates explainability methods to trace underlying theory assumptions back to the input feature space - specifically the x-dependence of PDFs - thereby identifying the salient features that shape constraints and model interpretations. Additionally, this work aims to enhance the incorporation of lattice inputs in phenomenological fits and refine the consistency between lattice QCD and collider phenomenology. Together, these components point towards uncertainty-aware autonomous workflows, pushing the frontier of particle physics discovery.
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