Abstract

Extragalactic spectroscopy provides us with information about the properties of galaxies and the fundamental nature of the universe. By analyzing galaxy spectra, we gain insight into a galaxy’s evolution through measures of its star formation rate (SFR), metallicity (Z), ionization parameter (U), gas pressure, dust extinction, electron density, and redshift (z). Redshift, in particular, can provide us with information about a galaxy’s velocity and distance, the formation of baryonic matter, dark matter halos, and the variation of galaxy properties with redshift. The analysis of these spectra is often an incredibly demanding, time-intensive, and potentially inaccurate process, further complicated by the explosion in the size and number of spectroscopic surveys and their resulting datasets over the last decade. Automated techniques, such as machine learning (ML) and citizen science, offer two distinct approaches to handling these large datasets. This thesis lays the foundation for combining the two approaches and for developing an emission line detection algorithm that will be trained on data from the Redshift Wrangler citizen science project and integrated into the existing ML model, SpecPT.

Publication Date

8-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Astrophysical Sciences and Technology (MS)

Department, Program, or Center

Physics and Astronomy, School of

College

College of Science

Advisor

Jeyhan Kartaltepe

Campus

RIT – Main Campus

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