83 citations · 85 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
pylhe: A Lightweight Python interface to Les Houches Event files
Alexander Puck Neuwirth, Matthew Feickert, Lukas Heinrich +1
Les Houches Event files are a standard format for Monte Carlo event generators in high-energy physics. pylhe is a lightweight pure-Python library for reading and writing LHE event…
Reinterpretation and preservation of data and analyses in HEP
Jon Butterworth, Sabine Kraml, Harrison Prosper +145
Data from particle physics experiments are unique and are often the result of a very large investment of resources. Given the potential scientific impact of these data, which goes…
Data and Analysis Preservation, Recasting, and Reinterpretation
Stephen Bailey, Christian Bierlich, Andy Buckley +15
We make the case for the systematic, reliable preservation of event-wise data, derived data products, and executable analysis code. This preservation enables the analyses' long-ter…
A Living Review of Machine Learning for Particle Physics
Matthew Feickert, Benjamin Nachman
Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we hav…
Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov +139
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpret…