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, 2) create a low-scatter galaxy cluster dynamical mass proxy using symbolic regression, and 3) develop a deep learning approach for estimating galaxy cluster ellipticity. The goal is to address tensions in the modern cosmological model by leveraging advanced data science techniques to extract insights from the rich data sets of upcoming optical surveys. Additionally, this award supports the creation of play-based STEM lesson plans on topics like light, shadows, and eclipses for early elementary students.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $228.4k | 7/31/23 |