| NSF Org: |
AST Division Of Astronomical Sciences |
| Recipient: |
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| Initial Amendment Date: | August 7, 2020 |
| Latest Amendment Date: | August 7, 2020 |
| Award Number: | 2007013 |
| Award Instrument: | Standard Grant |
| Program Manager: |
ANDREAS BERLIND
aberlind@nsf.gov (703)292-5387 AST Division Of Astronomical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | September 1, 2020 |
| End Date: | August 31, 2025 (Estimated) |
| Total Intended Award Amount: | $385,219.00 |
| Total Awarded Amount to Date: | $385,219.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
801 UNIVERSITY BLVD TUSCALOOSA AL US 35401-2029 (205)348-5152 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Tuscaloosa AL US 35404-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
EXTRAGALACTIC ASTRON & COSMOLO, EPSCoR Co-Funding |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
The history of the formation of a galaxy like our own Milky Way is recorded in the properties of gas between the visible stars in the galaxy. The gas is in low density clouds and is difficult to study. This surrounding gas is usually detected by observing its silhouette in the light of background quasars, known as ?quasar absorption lines?. The investigators will develop tools to help astronomers to better interpret the chemical history of this gas. They will develop tools to use quasar absorption lines to determine the gas composition, temperature and density. These tools will better determine where the gas clouds are in these galaxies and how clouds of gas are moving. By comparing the properties of these clouds of gas, they seek to understand the history of galaxy?s transformation of hydrogen and helium into more complex atoms. During this study, the investigators will train graduate and undergraduate students in a variety of astronomical and machine learning techniques. The investigators will reach out to K-12 students and the community at large.
The investigators will generate synthetic quasar absorption spectra from galaxy formation simulations for a range of galaxy masses and properties. They will analyze these simulated spectra in the same manner as astronomical observations. By correlating observed spectra with synthetic absorption systems, they will determine the conditions responsible for the absorption. In the simulations the particles will be tagged by their physical properties (phase, chemical composition, 3D location, kinematics) and history (e.g. from a galactic outflow or part of pristine in-fall). Using state-of-the-art algorithms including a machine learning approach, they will provide a probabilistic mapping between the observables and the true physical properties and history of the gas. The resulting mapping code will be tested on real observations and provided to the astronomical community to enable them to interpret current and future observations. This project will combine expertise in galaxy simulations, observational quasar spectroscopy, and machine learning, and provide new research and outreach opportunities in a geographical region underrepresented in astrophysics.
This project is jointly funded by the Astronomy Division and the Established Program to Stimulate Competitive Research (EPSCoR).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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