Project Grant 2206339

Award Date 9/1/22
Completion Date 8/31/25
Dollars Obligated $361K
Federal Grant Program
47.049
Assistance Type
Project Grant
Place of Performance
Ithaca, NY, USA
Similar Awards
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,...
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 National Science Foundation (NSF) awarded a $207,876 Project Grant under the Mathematical and Physical Sciences (CFDA 47.049) program to President and Fellows of Harvard College. This grant will support the development, testing, and application of machine learning (ML) algorithms and pipelines for the photometric classification of optical transients from current and future astronomical surveys. The key products and services to be delivered under this project include: (i) combining...
This Project Grant award from the National Science Foundation Division of Astronomical Sciences provides $403,383 to President And Fellows Of Harvard College for research and application development under the Mathematical and Physical Sciences program (CFDA 47.049). Specifically, the award supports collaborative research to develop machine learning techniques for classifying optical transients detected through astronomical observation. The research is being conducted between September 2021 and...
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 $586,924 National Science Foundation project grant supports the development of scalable and efficient astronomical data processing software among distributed observatories. Funded under the Mathematical and Physical Sciences program (CFDA 47.049), the University of Virginia team will design system software focusing on performance measurement and analysis of data processing jobs run on the National Radio Astronomy Observatory's Common Astronomy Software Applications and machine/deep learning...
This $304,670 project grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support the development of a framework for analyzing time-resolved spectroscopic data. The awardee, Cornell University Office of Sponsored Programs, will develop algorithms to disentangle stellar activity signals from planetary signals in time-resolved spectral data from searches for Earth-like exoplanets. Specifically, the project aims to model single or multiple...
The University of California, Davis received a $200,000 Project Grant award from the National Science Foundation Division of Mathematical Sciences. The grant supports the collaborative research project "Advancing Statistical Foundations and Frontiers for and from Emerging Astronomical Data Challenges" from July 1, 2021 to June 30, 2024. The project aims to advance statistical methods and techniques to address data analysis challenges arising in astronomy, as the field generates...
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...
This $1,969,052 Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports the development of an open-source software library to enable coordinated follow-up observations across diverse astronomical observatories. The project, led by the Las Cumbres Observatory Global Telescope Network, aims to streamline the process of requesting observations from optical, infrared, radio, X-ray, and ultraviolet telescopes in response to time-domain...

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 across multiple bands, as well as Bayesian neural networks leveraging improved priors and exploration of solution spaces. Companion components address optimal specification and use of source likelihood functions, more accurate representations of source uncertainty, enhanced detection of dim variable objects, and demographic modeling of cosmic populations. All algorithms will be implemented in open-source software useful for diverse ground- and space-based surveys. Awarded September 1, 2022 to Cornell University's Office of Sponsored Programs, this grant supports the collaborative research of astronomers, applied mathematicians, and statisticians to advance data science solutions for astronomy.

Generated 1/6/24, 11:04 AM