12 papers
Towards Engineering Scaling Laws with Pretraining Data Composition
Jan-Lucas Uslu, Kevin Greif, Daniel Whiteson +1
Neural scaling laws describe how model performance improves as a power law in compute, model size, and dataset size. While well-established for large language models, these relatio…
Optimal Transport Event Representation for Anomaly Detection
Tianji Cai, Aditya Bhargava, Benjamin Nachman
We introduce optimal transport (OT) as a physics-based intermediate event representation for weakly supervised anomaly detection. With only injection of resonant signals in…
Parnassus: A GPU-enabled, Python-based Package for Fast Particle Detector Simulation and Reconstruction
Abdelrahman Elabd, Eilam Gross, Dmitrii Kobylianskii +1
We present the public software release of Parnassus, a Python/PyTorch, GPU-compatible framework for fast detector simulation and reconstruction in particle and nuclear physics. Par…
An AI-ready, Polarized Electron-Positron Collision Dataset
Chi Lung Cheng, Simon Corrodi, T. J. Hobbs +2
We present a modernized, AI-ready release of reconstructed data from the SLD experiment at the SLAC Linear Collider (SLC). The dataset comprises approximately 660{,}000 reconstruct…
Low Activity Tritium Detection in CCDs Using Deep Learning Techniques
E. Rofors, R. Heller, R. J. Cooper +4
This study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonprolifera…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…