IRIS-HEP is a software institute funded by the National Science Foundation. It aims to develop the state-of-the-art software cyberinfrastructure required for the challenges of data intensive scientific research at the High Luminosity Large Hadron Collider (HL-LHC) at CERN, and other planned HEP experiments of the 2020’s. These facilities are discovery machines which aim to understand the fundamental building blocks of nature and their interactions.

The Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) has been established to meet the software and computing challenges of the HL-LHC, through R&D for the software for acquiring, managing, processing and analyzing HL-LHC data. IRIS-HEP will address key elements of the “Roadmap for HEP Software and Computing R&D for the 2020s” and is implementing the “Strategic Plan for a Scientific Software Innovation Institute (S2I2) for High Energy Physics” submitted to the NSF in December 2017. These two documents represent, respectively, the outcome of international and U.S. HEP community planning processes; these were driven in part by the NSF-funded S2I2-HEP Institute Conceptualization Project. This project advances the objectives of the National Strategic Computing Initiative (NSCI) and the objectives of “Harnessing the Data Revolution”, one of the 10 Big Ideas for Future NSF Investments.

IRIS-HEP is as an active center for software R&D, functions as an intellectual hub for the larger community-wide software R&D efforts, and aims to transform the operational services required to ensure the success of the HL-LHC scientific program. Three high-impact R&D areas are working to leverage the talents of the U.S. university community:

  • development of innovative algorithms for data reconstruction and triggering;
  • development of highly performant analysis systems that reduce `time-to-insight’ and maximize the HL-LHC physics potential; and
  • development of data organization, management and access systems for the community’s upcoming Exabyte era.

Although it is not one of the funded focus areas, applications of machine learning was one aspect of the “Strategic Plan for a Scientific Software Innovation Institute (S2I2) for High Energy Physics” which was a result of a community planning exercise. IRIS-HEP pursues some modest exploratory R&D in this area.

Collaborators Include:

Peter Elmer

Princeton University

Senior Research Physicist

Mike Sokoloff

University of Cincinnati

Professor of Physics

Gowtham Atluri

University of Cincinnati

Assistant Professor of Electrical Engineering and Computer Science

Mike Williams

Massachusetts Institute of Technology

Associate Professor of Physics

Kyle Cranmer

New York University

Professor of Physics

See the project website for a full listing of the collaborators on this project.

Project Website

Related Funding

  •    NSF OAC-1836650