Classifying Stellar Variability in the V and g bands with the All-Sky Automated Survey for SuperNovae
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Date
2021-05
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The Ohio State University
Abstract
The All-Sky Automated Survey for SuperNovae (ASAS-SN) is a wide-field photometric survey that monitors the entire night sky every night. Recently, it has begun observing in the g-band with an improved cadence (< 24 hours) and reduced diurnal aliasing compared to their survey in the V-band, allowing for up to 100 million Milky Way stars to be observed. Many candidates for variable stars have been recovered which require classification based on their time-domain photometric modulations. ASAS-SN currently uses a machine-learning classifier that utilized Fourier transforms to classify variable candidates in their data. However, machine learning classifiers are prone to incorrectly classifying rare phenomena and objects that are the result of systematic errors in detection. Here, I present additional methods of classification including Citizen ASAS-SN and a different approach to machine learning classification. Citizen ASAS-SN is a citizen science project hosted on the Zooniverse platform in which volunteers are presented with ASAS-SN g-band light curves of variable star candidates. The classification workflow allows volunteers to classify these sources, while also allowing for the identification of unique variable stars for additional follow-up. We aim to improve existing and future variable star classifications in our time series catalog by implementing data from Citizen ASAS-SN and updating our machine learning classifiers with promising results shown in this thesis.
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Keywords
Variable Stars, Light Curve Classification, Machine Learning