Speaker
Description
A(i)DAPT, or AI for Data Analysis and PreservaTion, is a CLAS group, the goal of which is to re-analyze and improve upon the measurements from past CLAS experiments using machine learning. This project utilizes deep learning techniques, specifically generative adversarial networks (GANs), to improve Monte Carlo methods used in the analysis of data. Producing simulations with generative AI as opposed to traditional simulation software is significantly faster and more computationally efficient. Thus, there is an appreciable demand for machine learning approaches to assist with (or possibly replace) these necessary tasks in nuclear and particle physics. The Jefferson Lab Data Science Group became involved with A(i)DAPT in an effort to help streamline, generalize, and optimize the framework already successfully operating within the group. In this talk, I will focus on the contributions from the Data Science Group towards achieving these goals.
