by Austin Strickler

Austin Strickler is an undergraduate Freshman at The Pennsylvania State University. He’s studying Astronomy and Astrophysics and undertook a STAR (Students Together for Astronomy Research) project led by graduate student Chloe Klare. The project focuses on obtaining important astronomical information in a ground-up approach without relying on previously made software. Outside of academics, Austin is an avid runner and has run Cross Country since Middle School. He also enjoys camping and is an Eagle Scout. Austin intends to pursue a PhD in astronomy and is starting a new research project with a Penn State faculty member in the summer.

Calculating the redshift of galaxies is a fundamental step that allows us to understand the galaxy’s distance from us. But how do astronomers calculate redshift—and how do they use this to classify them? In this study, I used a hands-on approach of spectroscopic analysis to answer these questions from the ground up.

I started by writing a program to identify spectral lines in a sample of galaxies from the Sloan Digital Sky Survey (SDSS). This program computed the redshift by matching the observed, redshifted lines to a table of rest-frame spectral lines. I optimized this program to improve the correctly calculated redshifts in a sample of 10,000 galaxies from 30% to 70%. After building this, I realized I could achieve even greater accuracy by using a method called chi-squared optimization with templates. Like the self-made astronomers of the past, I made my own: I hand-picked galaxies, grouped them by their spectral shape and lines, and then combined them into 5 preliminary templates. I re-ran these templates over the whole sample to further refine their shape and improve their classification. Moving forward, I plan to further refine these methods of spectroscopic analysis, which is ever more important in the era of modern survey astronomy.

Edited by: Sumeet Kulkarni