About Me

I am an Astronomer working at The Ohio State University. I work as a Data Analyst with The All-Sky Automated Survey for SuperNovae (ASAS-SN) as well as a collaborator and researcher for Citizen ASAS-SN. My primary research interests include time-domain astronomy with a focus on stellar variability.

I graduated summa cum laude from Ohio State in May 2021, with a Bachelor's degree in Physics and Astronomy & Astrophysics with research distinction in Astronomy & Astrophysics. As an undergraduate, I worked with Dr. Kris Stanek and Tharindu Jayasinghe to study variable stars using citizen science.

Research

Citizen ASAS-SN

For some background, The All-SKy Automated Survey for SuperNovae (ASAS-SN) is a wide-field photometric survey that monitors the entire night sky every night. Recently, ASAS-SN has shifted its survey to the g-band, allowing for up to 100 million Milky Way stars to be observed. When compared to their previous surveys, the new g-band data is especially promising for the discovery of new variable stars. Many of these variable stars can be classified by the shape of their light curves alone which was the main motiavtion for this type of work.

During this project's development, I was one of the lead collabortaors that helped design, test, and implement ASAS-SN's data into a functioning citizen science project. During this time, I designed tutorials, field guides, and workflows for users to navigate. I generated ~75,000 light curves for the periodic variable candidates in ASAS-SN's new g-band catalog.

The name of this project is Citizen ASAS-SN and it is currently available on the Zooniverse platform which is one of the world's largest hubs for citizen science. The power of Citizen science comes from its ability to harness the power of the crowd, which makes it the perfect tool for finding unusual phenomena that machine classifiers often miss. Here, our aim is to find the most unusual variable stars in the milky way. Specifically, we primarily focus on the classification of g-band light curves. Volunteers working on Citizen ASAS-SN will be shown images of light curves and tasked to determine their variable class.

In ASAS-SN's V-band catalog, machine learning techniques were utilized to classify variable candidates. However, some of these candidates are simply the result of systematic errors from detection or false positives due to signal contamination. Such instances are prone to be incorrectly classified as real variable phenomena. When training new classifiers, previously cataloged variables are used as training sets. If the training set contains "junk" data, the classifier will suffer in performance. Using data from Citizen ASAS-SN, we can more effectively remove these "junk" variables from our candidate sets and improve existing and future classifications.

ADS Link

RNAAS Link

Variable Stars in the V and g bands

ASAS-SN's shift to surveying in the g-band allows us to examine variability features for any particular star in both the V and g bands. Most variable stars exhibit correlated variability patterns in the different wavelengths. Using the newer observations in g allows us to refine our detection for new variables, refine existing period measurements, and distinguish microvariability trends from noise ( Süveges et al., 2012). This analysis served as the foundation for my undergraduate thesis which can be accessed below.

OSU Knowledge Bank

Data Analyst

Currently, I am working as a Data Analyst for ASAS-SN. ASAS-SN aquires data from a network of telescopes distributed around the globe. These units regularly survey the entire visible sky down to g~18 mag and forward data through our data processing pipeline. Working with Professors Kris Stanek and Chris Kochanek, I maintain the quality control of the nightly images from the ASAS-SN telescopes and analyze various transient candidates (supernovae, cataclysmic variables, etc.) for follow up and public release.

Curriculum Vitae

You can access my CV below.

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