Speaker
Description
Uncertainty quantification (UQ) plays a crucial role in the predictive power of nonperturbative quantum correlation functions at high precision. My research explores new approaches to UQ in the context of parton distribution functions (PDFs), using machine learning techniques to map between observables and underlying theoretical models, and navigate the complex parametric landscape of phenomenological global fits including beyond the Standard Model (BSM) physics scenarios. By leveraging variational autoencoders (VAEs) and contrastive learning with similarity metrics, I investigate how the inherent uncertainties in fits of collinear PDFs impact the landscape of new physics models. Incorporating novel methods such as evidential deep learning, we define a new information theory metric to understand parametric theory overlaps and redundancies. My work aims to enhance our understanding of nonperturbative QCD through next generation machine learning models, ultimately pushing the frontier of particle physics discovery.
