Project Grant 2206340
- 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 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 Project Grant award from the National Science Foundation (NSF) Directorate for Mathematical and Physical Sciences supports the development of advanced artificial intelligence (AI) systems to optimize the search for gravitational wave events accompanied by electromagnetic radiation. The $160,476 award to the California Institute of Technology (Caltech) will fund the creation of an AI agent that can adaptively learn to make optimal decisions for follow-up observations of these rare and...
- 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 $306,464 Project Grant awarded by the National Science Foundation's (NSF) Division of Astronomical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program will develop "DECIPHER: Deep Computer Vision in the Astrophysics of Planet Formation." The project aims to create a machine learning tool that can detect and measure the mass of exoplanets forming in protoplanetary disks from observational data. This cross-disciplinary effort will leverage advances in...
- The California Institute of Technology (Caltech) received a $638,136 Project Grant award from the National Science Foundation (NSF) Division of Astronomical Sciences on August 1, 2021 to conduct a systematic census of active galactic nuclei (AGN) variability through July 31, 2024. The grant is part of the NSF's Mathematical and Physical Sciences program (CFDA 47.049), which supports advancing scientific knowledge and enhancing understanding of major problems through progress in mathematics and...
- 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...
- This $103,360 Project Grant awarded by the National Science Foundation (NSF) Division of Astronomical Sciences supports a collaborative research program to use artificial intelligence (AI) to detect a large sample of the faintest and smallest galaxies in the universe. The program, part of the NSF's Mathematical and Physical Sciences (CFDA 47.049) research portfolio, will leverage a convolutional neural network image classifier to build an unbiased sample of distant dwarf galaxies. The research...
- This $457,976 Project Grant awarded by the National Science Foundation's (NSF) Division of Astronomical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports research to develop a novel framework for constraining models of baryonic feedback in the universe. The research team at the University of Chicago will apply artificial intelligence and machine learning techniques to data from cosmic microwave background and galaxy surveys to gain insights into the...
- 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...
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 of variable and transient astronomical sources from current and future digital sky surveys. Key products include advanced light curve classifiers using Bayesian neural networks and improved source likelihood functions. The grant will also address optimal specification and representation of source uncertainty, detection of dim variable objects, and demographic modeling. One graduate student will be trained in applied mathematics for data science problems in astronomy. Overall, this award aims to enhance partnership between astronomers, applied mathematicians, and statisticians to extract greater science from astronomical survey data through new machine learning technologies.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $111.0k | 8/18/22 |