6 papers
Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector
Cristiano Fanelli, James Giroux, Cole Granger +1
We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified framework for fast simulation, pa…
Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning
Carlos Cardona-Giraldo, Cristiano Fanelli, James Giroux +3
Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of avail…
Physics-Aware, Shannon-Optimal Compression via Arithmetic Coding for Distributional Fidelity
Cristiano Fanelli
Assessing whether two datasets are distributionally consistent is central to modern scientific analysis, particularly as generative artificial intelligence produces synthetic data…
Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data
James Giroux, Cristiano Fanelli
We present a (proto) Foundation Model for Nuclear Physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future Electron Ion Collider. B…
Generative Models for Fast Simulation of Cherenkov Detectors at the Electron-Ion Collider
James Giroux, Michael Martinez, Cristiano Fanelli
The integration of Deep Learning (DL) into experimental nuclear and particle physics has driven significant progress in simulation and reconstruction workflows. However, traditiona…
Uncertainty Quantification with Bayesian Higher Order ReLU KANs
James Giroux, Cristiano Fanelli
We introduce the first method of uncertainty quantification in the domain of Kolmogorov-Arnold Networks, specifically focusing on (Higher Order) ReLUKANs to enhance computational e…