Available at: https://digitalcommons.calpoly.edu/theses/3260
Date of Award
6-2026
Degree Name
MS in Mathematics
Department/Program
Mathematics
College
College of Science and Mathematics
Advisor
Paul Choboter
Advisor Department
Mathematics
Advisor College
College of Science and Mathematics
Abstract
Slitless spectroscopy with the Hubble Space Telescope's Wide Field Camera 3 \linebreak (WFC3/IR) enables simultaneous spectral coverage of all sources in a field, but large-scale single-orientation datasets remain challenging to process. We developed a pipeline that automates HSTaXe spectral extraction across hundreds of Hubble Space Telescope observations, enabling efficient processing of 84 fields and producing over 50,000 spectra from three filters (F110W, F125W, and F160W) and two grisms (G102 and G141). This was achieved by creating a companion Python package, the Module for Infrared Label Analysis (MILA), that integrates data calibration, extraction, and visualization. To assess the scientific utility of the extracted spectra, we performed stellar label determination using The Cannon. Spectra from multiple grisms were stitched and normalized onto a common wavelength grid to enable consistent label determination. We validated label recovery for Red Giant Branch stars with known stellar parameters, and then repeated validation for the full spectral range. Finally, we recovered effective temperature, surface gravity, and metallicity for 411 survey stars. The resulting spectral library serves as a testbed for the Wide Field Instrument on the Nancy Grace Roman Space Telescope, where similarly large slitless spectroscopic datasets will be routine. These results confirm that large-volume, data-driven stellar characterization is feasible with slitless infrared spectroscopy. Our next step is to optimize the stellar label determination process for metallicity recovery and apply the pipeline to more datasets from the Hubble Space Telescope.