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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2024

AIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics (Final Report)

The FAIR4HEP project was a collaboration between Argonne National Laboratory, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, University of Minnesota, and the University of California San Diego funded by the US Department of Energy, Office of Science, Office of Advanced Scientific Research (ASCR) from 2020 to 2023. The primary focus of the FAIR4HEP project was to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of findable, accessible, interoperable, and reusable (FAIR) frameworks. Using high-energy physics (HEP) as the science driver, this project developed a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques and provide an environment where novel approaches to AI can be explored. This final report summarizes the accomplishments of the MIT group.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

FAIR Framework for Physics-Inspired Artificial Intelligence in High Energy Physics (Final Report)

The FAIR4HEP project was a collaboration between Argonne National Laboratory, University of Illinois Urbana-Champaign, Massachusetts Institute of Technology, University of Minnesota, and University of California San Diego funded by the US Department of Energy, Office of Science, Office of Advanced Scientific Research (ASCR) from 2020 to 2024. The primary focus of the FAIR4HEP project was to advance our understanding of the relationship between data and artificial intelligence (AI) models by exploring relationships among them through the development of findable, accessible, interoperable, and reusable (FAIR) frameworks. Using high-energy physics (HEP) as the science driver, this project developed a FAIR framework to advance our understanding of AI, provide new insights to apply AI techniques, and provide an environment where novel approaches to AI can be explored. This final report summarizes the accomplishments of the University of Illinois group.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

FAIR Framework for Physics-Inspired AI in High Energy Physics (Final Technical Report)

The main deliverable of this proposal was to publish data from high energy physics experiments in a FAIR format so that non-specialists could develop machine learning technologies using our data. The Minnesota team of Profs. Cushman, Furmanski and Rusack, from the high energy experiments CDMS, Micro-Boone and CMS, respectively, and Prof J. Sun from Computer Science worked to organize the data, to provide code to access the data, and where relevant provide documentation describing the data. The FAIR4HEP collaboration was formed with groups from UC San Diego, MIT, and the University of Illinois, with the principal investigator was Dr. Huerta. Collectively we collaborated on the publication of datasets from the LHC experiments. Members of the Minnesota group contributed to the common papers published by the collaboration

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Identifying Interaction Location in SuperCDMS Detectors

The Super Cryogenic Dark Matter Search (SuperCDMS) experiment uses silicon and germanium particle detectors operated at temperatures of ∼ 30 mK to search for Weakly Interacting Massive Particles (WIMPs), which are candidate dark matter particles that interact weakly with nuclei in the detectors. In operating these detectors, it is required not only to measure the energy of the interaction between the WIMP and the nuclei, but also to reconstruct where the interaction occurred, as the location can be used to separate background interactions from signal and to correct for variations with the location of the energy response. In this project, we, as a team from the University of Minnesota, aim to address the problem of accurately reconstructing the locations of interactions in the SuperCDMS detectors using machine learning methods. The dataset we provided here includes interactions at thirteen different locations from test data taken at the University of Minnesota. For each interaction, a set of parameters was extracted from the signals from each of the five sensors. These parameters represent information known to be sensitive to interaction location, including the relative timing between pulses in different channels, and features like the pulse shape. The relative amplitudes of the pulses are also relevant but due to instabilities in amplification during the test, this data is not included. The parameters included for each interaction are described in our project document. For more details, feel free to check our Github page: https://fair-umn.github.io/FAIR-UMN-CDMS/

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Laser Response in ECAL Crystals in CMS Detector

The dataset contains the Laser responses of the Lead-Tungstate crystals in the Electromagnetic Calorimeter (ECAL) of the CMS Experiment recorded during the Run 2 (2016-2018) of LHC running. The datasets consists of two tar folders: one corresponding to the "plus" side of the detector and one corresponding to the "minus" side. Each folder contains files in csv format, each file corresponding to the histories of all crystals in each "ieta" ring. The detailed description of the columns can be found under the section names "dataset" on Github pages at https://fair-umn.github.io/fair_ecal_monitoring.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Electron Energy Regression in High-Granularity Calorimeter Prototype

The dataset consists of simulations of calibrated reconstructed hits produced by a positron passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the positrons with energy ranging from 10 to 350 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Pion Energy Regression in High-Granularity Calorimeter Prototype

The dataset consists of simulations of calibrated reconstructed hits produced by a pion passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the pions with energy ranging from 10 to as high as 500 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.

FAIR4HEP↗