About Me
Education: Current Sophomore at the University of Washington in Seattle, WA. I am pursuing a double major in physics and astronomy, with minors in aeronautics and astronautics, as well as
applied mathematics.
Research Interests: I am broadly interested in novel methods to study star formation in galaxies. As of now, this includes manifold dimension reduction to map
star forming clumps in high-redshift lensed galaxies. I am also interested in supermassive black hole accretion discs, relativistic jets, probing neutron star equations of state,
computational astronomy (namely simulations), and radio astronomy.
Hobbies: Mountaineering, Trombone, Golfing, Weight-lifting, filling out applications
Publications: Coming soon! Include ADS link!
Research
JWST LEGGOS
Strong gravitational lensing provides a natural magnifying effect,
allowing for detailed studies of high-redshift star-forming clusters. While there have been studies on the
physical properties of these clumps in strongly lensed galaxies, there is a distinct lack of software to automatically
identify and analyze regions of stellar formation. Typical methods of clump identification rely on contrast enhancement
through image smoothing and subtraction, followed by the use of visual and automatic source detection software.
While generally effective, these approaches require careful parameter tuning and manual validation,
limiting their efficiency and reproducibility. These methods also suffer from inaccuracies due to contamination from the
diffuse host galaxy, bright neighboring sources, and intracluster light. We present a novel software pipeline titled SUMAC
(Software for Uniform Manifold Approximation of Clusters) that automatically processes FITS files of lensed galaxies, reduces
the data using Uniform Manifold Approximation and Projection (UMAP), and outputs a topological map clustering together pixels with
similar characteristics. Users can specify parameters of interest, including flux, spectral energy distribution, and morphology.
We utilize JWST/NIRCam imagery of the z =2.481 lensed galaxy SGAS1110, confirming the functionality of SUMAC by automatically
tagging points in the UMAP topological space, mapping them back to the imagery of the lensed galaxy to show alignment with
visual star forming clusters. We additionally analyze spectroscopic data for the galaxy, ensuring pixels that SUMAC identifies as
corresponding to star-forming clumps match characteristics such as age, metallicity, and emission line ratios that are
indicative of stellar formation. SUMAC’s ability to handle large datasets efficiently, without requiring manual
validation or extensive parameter tuning, ensures a more reproducible and scalable approach to high-redshift galactic
analysis. SUMAC represents a significant advancement in the field of astronomical image processing, offering a powerful
tool for studying early universe galactic dynamics.
Probing the Neutron Star Equation of State via the Variational Autoencoder - The Institute for Nuclear Theory
Contact
Email: [email protected]
