Speaker
Description
Uncertainty quantification (UQ) plays a crucial role in the predictive power of nonperturbative quantum correlation functions in high precision phenomenology. My research explores novel 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 scenarios such as beyond the Standard Model (BSM) scenarios. By leveraging variational autoencoders (VAEs) and contrastive learning with similarity metrics, I investigate how the inherent uncertainties in phenomenological fits of collinear PDFs impact the landscape of 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. This allows for the identification of salient features that shape fits and model interpretations, providing new insights into the role of theory assumptions in comprehensive phenomenological fits. Furthermore, the lessons from uncertainty quantification in PDFs can inform studies of multi-dimensional quantum correlation functions such as generalized parton distributions (GPDs), connecting these tools to a broader phenomenological framework in QCD. My work aims to enhance the incorporation of lattice inputs and refine our understanding of nonperturbative QCD through next generation machine learning models, ultimately pushing the frontier of particle physics discovery.