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Found 11 papers, 5 papers with code
Uncertainty Quantification with Bayesian Higher Order ReLU KANs
We introduce the first method of uncertainty quantification in the domain of Kolmogorov-Arnold Networks, specifically focusing on (Higher Order) ReLUKANs to enhance computational efficiency given the computational demands of Bayesian methods.
Unmasking Social Bots: How Confident Are We?
Social bots remain a major vector for spreading disinformation on social media and a menace to the public.
Deep(er) Reconstruction of Imaging Cherenkov Detectors with Swin Transformers and Normalizing Flow Models
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.
Physics Event Classification Using Large Language Models
The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America.
Towards a RAG-based Summarization Agent for the Electron-Ion Collider
The complexity and sheer volume of information encompassing documents, papers, data, and other resources from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.
ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering
We introduce a physics-informed Bayesian Neural Network (BNN) with flow approximated posteriors using multiplicative normalizing flows (MNF) for detailed uncertainty quantification (UQ) at the physics event-level.
Design of Detectors at the Electron Ion Collider with Artificial Intelligence
Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines.
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
Machine Learning for Imaging Cherenkov Detectors
Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands.
DeepRICH: Learning Deeply Cherenkov Detectors
Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data.
Double Polarization Observables in Pentaquark Photoproduction
We investigate the properties of the hidden charm pentaquark-like resonances first observed by LHCb in 2015, by measuring the polarization transfer KLL between the incident photon and the outgoing proton in the exclusive photoproduction of J/psi near threshold.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment
