18 papers
The Living Guide of Machine Learning for Particle Physics
Claudius Krause, Ramon Winterhalder, Matthew Feickert +1
We started the Living Review of Machine Learning for Particle Physics (HEP-ML Living Review) in 2020 as a community-maintained, near-comprehensive bibliography of machine learning…
Predict before you train: Scaling Laws for particle physics foundation models
Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute…
A Scientific Human-Agent Reproduction Pipeline
Joschka Birk, Gregor Kasieczka, Siddharth Mishra-Sharma +3
Reproducing scientific analyses is essential for preserving knowledge, building extensible codebases, and deepening researcher understanding - yet the effort often outweighs its ac…
Look everywhere effects in anomaly detection
Marie Hein, Benjamin Nachman, David Shih
Machine learning-based anomaly detection methods are able to search high-dimensional spaces for hints of new physics with much less theory bias than traditional searches. However,…
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
P. Alex May, Qibin Liu, Julia Gonski +1
Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well…