Data Science
Experimental Nuclear Physics
News
Cristiano Fanelli, James Giroux, Patrick Moran, Hemalata Nayak, Karthik Suresh, Eric Walter
https://arxiv.org/pdf/2404.05752.pdf [2404.05752v1 physics.data-an]
Information on the corresponding hackathon event can be found at https://eic.ai/hackathons
K. Suresh, N. Kackar, L. Schleck, C. Fanelli, arXiv:2403.15729v1 [cs.CL] https://arxiv.org/pdf/2403.15729.pdf
The app can be accessed through https://rags4eic-ai4eic.streamlit.app/
Link to 2023 Hackathon: Physics Event Classification Using Large Language Models (organized by C. Fanelli, J. Giroux, P. Moran, K. Suresh)
Our paper has been accepted to the NeurIPS 2023 workshop on Machine Learning and the Physical Sciences
Workshop website: https://ml4physicalsciences.github.ioOur Data Science team will collaborate in this project with W&M co-PI (Prof. Mordijck) to organize a yearly summer school on AI/ML for fusion energy at William & Mary.
Prof. Fanelli at W&M will be lead PI of the project AIDE (AI-assisted Detector design at the Electron Ion Collider). EIC is an accelerator project under construction at Brookhaven National Laboratory (BNL) that will probe the internal structure and forces of protons and neutrons that compose the atomic nucleus. This collaborative project involves co-PIs of national labs at Brookhaven National Lab and Jefferson Lab, as well as of other universities, Catholic University of America, and Duke University.
More info at:
Prof. Fanelli will be co-PI of the project AI-Optimized Polarization, led by Jefferson Lab. W&M will collaborate to provide a continuous AI/ML control for the polarized beam at the GlueX experiment in Hall D.
More info at:
https://content.govdelivery.com/accounts/USDOEOS/bulletins/36b5803
Lectures on AI/ML for Nuclear Physics and the Electron Ion Collider
More info at:
https://www.jlab.org/conference/hugs2023
https://cfteach.github.io/HUGS23/intro.html
