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Title: Citizen ASAS-SN: Citizen Science with The All-Sky Automated Survey for SuperNovae (ASAS-SN)
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Research Notes of the AAS
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Medium: X
Sponsoring Org:
National Science Foundation
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  1. 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 the value of Citizen ASAS-SN for identifying unusual variables with several examples. 
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  3. We present V -band photometry of the 20 000 brightest asteroids using data from the All-Sky Automated Survey for Supernovae (ASAS-SN) between 2012 and 2018. We were able to apply the convex inversion method to more than 5000 asteroids with more than 60 good measurements in order to derive their sidereal rotation periods, spin axis orientations, and shape models. We derive unique spin state and shape solutions for 760 asteroids, including 163 new determinations. This corresponds to a success rate of about 15%, which is significantly higher than the success rate previously achieved using photometry from surveys. We derive the first sidereal rotation periods for additional 69 asteroids. We find good agreement in spin periods and pole orientations for objects with prior solutions. We obtain a statistical sample of asteroid physical properties that is sufficient for the detection of several previously known trends, such as the underrepresentation of slow rotators in current databases, and the anisotropic distribution of spin orientations driven by the nongravitational forces. We also investigate the dependence of spin orientations on the rotation period. Since 2018, ASAS-SN has been observing the sky with higher cadence and a deeper limiting magnitude, which will lead to many more new solutions in just a few years. 
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