5 papers
Unknown Unknowns: Model Misspecification in Machine Learning for Physics
Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held +1
Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the quest…
PyHEP.dev 2024 Workshop Summary Report, August 26-30 2024, Aachen, Germany
Azzah Alshehri, Jan Bürger, Saransh Chopra +28
The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 20…
Physics analysis for the HL-LHC: concepts and pipelines in practice with the Analysis Grand Challenge
Alexander Held, Elliott Kauffman, Oksana Shadura +1
Realistic environments for prototyping, studying and improving analysis workflows are a crucial element on the way towards user-friendly physics analysis at HL-LHC scale. The IRIS-…
Machine Learning for Columnar High Energy Physics Analysis
Elliott Kauffman, Alexander Held, Oksana Shadura
Machine learning (ML) has become an integral component of high energy physics data analyses and is likely to continue to grow in prevalence. Physicists are incorporating ML into ma…
First performance measurements with the Analysis Grand Challenge
Oksana Shadura, Alexander Held
The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task i…