83 citations · 83 across the 2 of their papers we have counts for
8 papers
Learning from the Pandemic: the Future of Meetings in HEP and Beyond
Mark S. Neubauer, Todd Adams, Jennifer Adelman-McCarthy +36
The COVID-19 pandemic has by-and-large prevented in-person meetings since March 2020. While the increasing deployment of effective vaccines around the world is a very positive deve…
Distributed statistical inference with pyhf enabled through funcX
Matthew Feickert, Lukas Heinrich, Giordon Stark +1
In High Energy Physics facilities that provide High Performance Computing environments provide an opportunity to efficiently perform the statistical inference required for analysis…
Software Training in HEP
Sudhir Malik, Samuel Meehan, Kilian Lieret +44
Long term sustainability of the high energy physics (HEP) research software ecosystem is essential for the field. With upgrades and new facilities coming online throughout the 2020…
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…
Software Sustainability & High Energy Physics
Daniel S. Katz, Sudhir Malik, Mark S. Neubauer +16
New facilities of the 2020s, such as the High Luminosity Large Hadron Collider (HL-LHC), will be relevant through at least the 2030s. This means that their software efforts and tho…
The Scikit HEP Project -- overview and prospects
Eduardo Rodrigues, Benjamin Krikler, Chris Burr +9
Scikit-HEP is a community-driven and community-oriented project with the goal of providing an ecosystem for particle physics data analysis in Python. Scikit-HEP is a toolset of app…