Kyle Cranmer
University of Wisconsin-Madison, WI·Physics
Particle physics experiments produce enormous datasets and complex analyses. Their results are often hard for other scientists to reproduce, combine, or reinterpret when new theories come along.
- Fields
Physics
Computer Science
- Topics
high energy physics
particle physics
FAIR data
- Grants
- 1 known grant, through Sep 2027
What they work on
- Foster adoption of "living publications" in high energy physics, which bundle written results, data, and computational procedures so they can be examined, reproduced, and reused.
- Promote publishing likelihood functions and statistical models from particle physics analyses so results can be combined and reinterpreted, including in effective field theory analyses.
- Support preserving analysis workflows compatible with REANA, with a recast interface that allows kinematic reinterpretation and lets experiments fork and modify existing analyses.
More about the research
Making these research products findable, reusable, and preserved would let a single analysis keep answering new questions long after it is published. This project, FAIROS-HEP, is a research coordination network for high energy physics (HEP). It brings together members of the extended HEP community to spread practices and computing infrastructure for reusing data.
Why it matters
Preserving particle physics analyses as reusable "living publications" could let physicists test new theories, such as new particles or effective field theory effects, against existing data without repeating costly analyses. It could also save experiments effort by letting them build on earlier analyses rather than starting over.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
G J Peter Elmer
Princeton University, NJ·Physics
Develop new algorithms for reconstruction and triggering, which turn raw detector signals into particles and select which collisions to keep, for the ATLAS and CMS experiments at the High-Luminosity LHC.
Benjamin Nachman
Stanford University, CA·Physics
Develop an exchange format for machine-learning-based unbinned measurements stored as neural networks, and integrate it into an open-source platform that makes such data findable, accessible, interoperable and reusable (FAIR).
David Elbert
Johns Hopkins University, MD·Materials Science
Coordinate data producers, users, repositories and publishers through the Materials Research Coordination Network (MaRCN) to make depositing materials data in trusted repositories the norm under FAIR (findable, accessible, interoperable, reusable) principles.
The school
University of Wisconsin-Madison: 91 labs on PI Pages, $1.9B a year on research, 854 research doctorates a year.
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