Project Grant 2307158

Award Date 8/15/23
Completion Date 7/31/26
Dollars Obligated $560K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Santa Cruz, CA 95064, USA
Similar Awards
This three-year, $110,972 Project Grant from the National Science Foundation's Division of Astronomical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), will support the development of new statistical and machine learning methods and software to improve the detection, characterization, and classification of astrophysical objects using multi-epoch imaging survey data. The California Institute of Technology will apply innovative algorithms to optimize the discovery...
The University of California Santa Cruz will receive $273,625 under a Project Grant from the National Science Foundation Division of Astronomical Sciences to develop a framework for interpreting panchromatic observations of galaxy cluster halos. The award, made under the Mathematical and Physical Sciences program (CFDA 47.049), will support collaboration between researchers at UC Santa Cruz and the University of Maryland to combine data from the Dark Energy Spectroscopic Instrument, Atacama...
The National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences awarded a $228,435 Project Grant to The Johns Hopkins University to develop a toolkit of understandable machine learning (ML) methods for interpreting detailed optical galaxy surveys from the Rubin Observatory and Dark Energy Spectroscopic Instrument. This 3-year award, running from September 1, 2023 to August 31, 2026, aims to: 1) produce a statistical census of cosmological information at small scales,...
This three-year project grant from the National Science Foundation Division of Astronomical Sciences totals $360,645 to develop new statistical and machine learning methods and software to improve the detection, characterization, and classification of astrophysical objects using multi-epoch imaging survey data. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), key products include new algorithms for classifying variable and transient objects based on light curves...
This $421,175 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports a research project titled "The Emergence of Star Clusters: Insights from Artificial Intelligence." The project aims to better understand the physical mechanisms of star formation by analyzing data from the Atacama Large Millimeter Array and the James Webb Space Telescope to measure the timescales for clearing natal gas from newly formed stellar clusters....
This Project Grant award from the National Science Foundation (NSF) Division of Astronomical Sciences, under the Mathematical and Physical Sciences program (CFDA 47.049), provides $490,337 to the University of California, Santa Cruz (UCSC) from August 15, 2023 to July 31, 2026. The primary objective of this project is to develop novel data-driven machine learning techniques to enhance the computational performance and accuracy of numerical simulations for modeling relativistic plasma flows in...
This $309,517 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program (CFDA 47.049) will support research to develop advanced artificial intelligence (AI) methods for analyzing data from upcoming cosmological weak lensing surveys. The primary grantee, the University of California, Berkeley, will focus on simulation-based inference and generative learning approaches to extract cosmological parameters from distortions in galaxy images,...
This Project Grant from the National Science Foundation Division of Astronomical Sciences totals $134,673 to support research optimizing discovery with multi-epoch photometric survey data. Funded under the Mathematical and Physical Sciences program, the award will see the development of new statistical and machine learning methods and open-source software to improve detection, characterization, and classification of astrophysical objects in multi-epoch imaging surveys. A team of astronomers,...
The National Science Foundation (NSF) Division of Astronomical Sciences awarded a $497,792 Project Grant to the University of Arizona (doing business as the Arizona Board of Regents) to support research under the NSF's Mathematical and Physical Sciences program (CFDA 47.049). The grant aims to develop advanced machine learning techniques to extract cosmological information from mass maps generated by observing the gravitational lensing effects of dark matter. This research will enable more...
The National Science Foundation Division of Astronomical Sciences awarded a $463,511 Project Grant to the University of California, San Diego under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to support collaborative research titled "AI-Enhanced Exascale Simulations of the Earliest Galaxies" from September 1, 2021 through August 31, 2024. The University will utilize artificial intelligence and high-performance computing to conduct exascale simulations...

The National Science Foundation (NSF) Division of Astronomical Sciences awarded a $560,085 Project Grant to the University of California, Santa Cruz (UCSC) to apply an artificial intelligence/machine learning (AI/ML) model called MORPHEUS to analyze and classify astronomical objects in large-scale, public astronomical imaging surveys. The goals of this 3-year project are to further the understanding of the connection between galaxy morphology and the underlying physics of galaxy formation, as well as to lower the barrier to applying powerful AI/ML methodologies to astronomical datasets. UCSC will incorporate MORPHEUS into the Rubin Science Platform, validate it using a combination of Rubin and space-based data, and apply it to the initial LSST data releases. The project also supports Rubin Observatory science verification activities at UCSC.

Generated 5/14/24, 6:40 AM