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This content will become publicly available on February 1, 2023

Title: Citizen ASAS-SN Data Release. I. Variable Star Classification Using Citizen Science
Abstract We present the first results from Citizen ASAS-SN, a citizen science project for the All-Sky Automated Survey for Supernovae (ASAS-SN) hosted on the Zooniverse platform. Citizen ASAS-SN utilizes the newer, deeper, higher cadence ASAS-SN g -band data and tasks volunteers to classify periodic variable star candidates based on their phased light curves. We started from 40,640 new variable candidates from an input list of ∼7.4 million stars with δ < −60° and the volunteers identified 10,420 new discoveries which they classified as 4234 pulsating variables, 3132 rotational variables, 2923 eclipsing binaries, and 131 variables flagged as Unknown. They classified known variable stars with an accuracy of 89% for pulsating variables, 81% for eclipsing binaries, and 49% for rotational variables. We examine user performance, agreement between users, and compare the citizen science classifications with our machine learning classifier updated for the g -band light curves. In general, user activity correlates with higher classification accuracy and higher user agreement. We used the user’s “Junk” classifications to develop an effective machine learning classifier to separate real from false variables, and there is a clear path for using this “Junk” training set to significantly improve our primary machine learning classifier. We also illustrate more » the value of Citizen ASAS-SN for identifying unusual variables with several examples. « less
Authors:
; ; ; ; ; ; ; ; ;
Award ID(s):
1908570 1814440
Publication Date:
NSF-PAR ID:
10342147
Journal Name:
Publications of the Astronomical Society of the Pacific
Volume:
134
Issue:
1032
Page Range or eLocation-ID:
024201
ISSN:
0004-6280
Sponsoring Org:
National Science Foundation
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