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    Data Science

    Experimental Nuclear Physics


    ArXiv link: https://arxiv.org/pdf/2407.07376 

    Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future Electron-Ion Collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (dual-RICH) detector in the hadron direction, a

    Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the GlueX experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods and, for the first time, fast and accurate simulation. This advancement addresses a major bottleneck in Cherenkov detector simulations involving photon tracking through complex optical elements. Our results leverage advancements in Vision Transformers, specifically hierarchical Swin Transformer and normalizing flows. These methods enable direct learning from real data and the reconstruction of complex topologies. We conclude by discussing the implications and future extensions of this work, which can offer capabilities for PID for multiple cutting- edge experiments like the future EIC.


    Dr. Karthik Suresh, a current postdoctoral researcher in Data Science at William & Mary has been awarded the prestigious JSA Ph.D. Thesis Prize for his outstanding doctoral thesis at Jefferson Lab. Dr. Suresh completed his Ph.D. at the University of Regina, where he was co-supervised by Professor Zisis Papandreou and W&M Assistant Professor of Data Science, Cristiano Fanelli. He then transitioned to William & Mary to collaborate with Dr. Fanelli on a grant-funded project focused on AI-assisted Detector Design for the Electron Ion Collider.



    M. Diefenthaler, C. Fanelli, L. O. Gerlach, W. Guan, T. Horn, A. Jentsch, M. Lin, K. Nagai, H. Nayak, C. Pecar, K. Suresh, A. Vossen, T. Wang, T. Wenaus (AID (2) E collaboration)


    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 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: 
    Lectures on AI/ML for Nuclear Physics and the Electron Ion Collider 
    More info at: 

    Organizer of the AI4EIC workshop (W&M, 2022) – included sessions are (i) accelerator and detector design (EPIC and potentially detector-2), (ii) connections to theory, (iii) analysis, (iv) reconstruction and particle identification, (v) infrastructure and frontiers in AI/ML and (vi) streaming readout. 
    During the workshop we had AI/ML tutorial sessions provided by experts (academia, national labs, industry). 
    Organizer of the AI4EIC Hackathon (October 14, whole day event).
    More info: