COLLECTION OF PAPERS

Normalizing flows for domain adaptation when identifying Λ hyperon events (AI4EIC proceeding, JINST 19 C06020)

Photon classification with Gradient Boosted Trees at CLAS12 (AI4EIC proceeding, JINST 19 C06006)

Object condensation for track building in a backward electron tagger at the EIC  (AI4EIC proceeding, JINST 19 C05052)

Real-time charged track reconstruction for CLAS12  (AI4EIC proceeding, JINST 19 C05050)

Performance optimization for a scintillating glass electromagnetic calorimeter at the EIC (AI4EIC proceeding, JINST 19 C05049)

Towards a RAG-based Summarization Agent for the Electron-Ion Collider (AI4EIC proceeding, to appear on JINST, 2024)

Physics Event Classification Using Large Language Models (AI4EIC proceeding, to appear on JINST, 2024)

AI-Assisted Detector Design for the EIC (AID(2)E)  (AI4EIC proceeding, to appear on JINST, 2024)

Hydra: Computer Vision for Data Quality Monitoring  (AI4EIC proceeding, to appear on JINST, 2024)

ML-based Calibration and Control of the GlueX Central Drift Chamber  (AI4EIC proceeding, to appear on JINST, 2024)

Particle identification with machine learning from incomplete data in the ALICE experiment  (AI4EIC proceeding, to appear on JINST, 2024)

Beam Condition Forecasting with Non-destructive Measurements at FACET-II  (AI4EIC proceeding, to appear on JINST, 2024)

ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering The paper introduces a physics-informed Bayesian Neural Network with flow approximated posteriors using multiplicative normalizing flows for detailed uncertainty quantification at the physics event-level. The method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties and is applied to Deep Inelastic Scattering events to extract the kinematics (NeurIPS paper, arXiv:2310.02913)

Artificial Intelligence for the Electron Ion Collider (AI4EIC) This paper summarizes the different activities and R&D projects covered across the sessions of the 2nd AI4EIC workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community. (arXiv:2307.08593)

Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics  (arXiv:2210.06450)

High performance FPGA embedded system for machine learning based tracking and trigger in sPhenix and EIC  (link to JINST 17 C07003)

AI4EIC proceeding, Sep 2021

Machine learning on FPGA for event selection  (link to JINST 17 C06009)

AI4EIC proceeding, Sep 2021

Frontiers in computing for artificial intelligence  (link to JINST 17 C03037)

AI4EIC proceeding, Sep 2021

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider (link to arXiv:2205.09185 [physics.ins-det])

New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering (link to 2201.12197 [hep-ph])

JHEP 04 (2022) 084 DOI: 10.1007/JHEP04(2022)084

Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning (link to arXiv:2110.05505)

NIM-A 1025 (2022) 166164, 10.1016/j.nima.2021.166164

Artificial Intelligence for Imaging Cherenkov Detectors at the EIC (link to arXiv:2204.08645,; link to JINST 17 C07011)

AI4EIC proceeding, Sep 2021

AI for Experimental Controls at Jefferson Lab, (link to 2022_JINST_17_C03043)

AI4EIC proceeding, Sep 2021

Machine learning for track reconstruction at the LHC (link to 2022 JINST 17 C02026)

AI4EIC proceeding, Sep 2021

EIC Detector Overview (link to 2022 JINST 17 C02018, arXiv:2202.13970)

AI4EIC proceeding, Sep 2021

 Design of Detectors at the Electron Ion Collider with Artificial Intelligence (link to arXiv:2203.04530v2)

AI4EIC proceeding, Sep 2021

Machine Learning for the LHCb Simulation (link to arXiv:2110.07925

AI4EIC proceeding, Sep 2021

Accelerator and detector control for the EIC with machine learning (link to 2022 JINST 17 C02022)

AI4EIC proceeding, Sep 2021

Deeply Learning Deep Inelastic Scattering Kinematics (link to arXiv:2108.11638v2)

Revealing the structure of light pseudoscalar mesons at the electron–ion collider (link to arXiv:2102.11788v1, paper)

AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case (link to arXiv, paper)

DeepRICH: learning deeply Cherenkov detectors (link to arXiv:1911.11717, paper)

AI-based Monte Carlo event generator for electron-proton scattering, Alanazi, Y. et al., (link to arXiv:2008.03151v1 [hep-ph])

Nuclear Parton Distributions from Lepton-Nucleus Scattering and the Impact of an Electron-Ion Collider (link to arXiv 1904.00018, EPJ C)