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The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to monitor the entire sky, currently with a cadence of ≲ 24 h down to g ≲ 18.5 mag. ASAS-SN has routinely operated since 2013, collecting ∼ 2 000 to over 7 500 epochs of V- and g-band observations per field to date. This work illustrates the first analysis of ASAS-SN’s newer, deeper, and higher cadence g-band data. From an input source list of ∼55 million isolated sources with g < 18 mag, we identified 1.5 × 106 variable star candidates using a random forest (RF) classifier trained on features derived from Gaia, 2MASS, and AllWISE. Using ASAS-SN g-band light curves, and an updated RF classifier augmented with data from Citizen ASAS-SN, we classified the candidate variables into eight broad variability types. We present a catalogue of ∼116 000 new variable stars with high-classification probabilities, including ∼111 000 periodic variables and ∼5 000 irregular variables. We also recovered ∼263 000 known variable stars.

 

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.