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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The ASAS-SN catalogue of variable stars X: discovery of 116 000 new variable stars using G -band photometry
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.
more » « less- Award ID(s):
- 1908570
- PAR ID:
- 10391991
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 519
- Issue:
- 4
- ISSN:
- 0035-8711
- Format(s):
- Medium: X Size: p. 5271-5287
- Size(s):
- p. 5271-5287
- Sponsoring Org:
- National Science Foundation
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ABSTRACT The All-Sky Automated Survey for Supernovae (ASAS-SN) provides long baseline (∼4 yr) light curves for sources brighter than V ≲ 17 mag across the whole sky. As part of our effort to characterize the variability of all the stellar sources visible in ASAS-SN, we have produced ∼30.1 million V-band light curves for sources in the Southern hemisphere using the APASS DR9 (AAVSO Photometric All-Sky Survey Data Release) catalogue as our input source list. We have systematically searched these sources for variability using a pipeline based on random forest classifiers. We have identified ${\sim } 220\, 000$ variables, including ${\sim } 88\, 300$ new discoveries. In particular, we have discovered ${\sim }48\, 000$ red pulsating variables, ${\sim }23\, 000$ eclipsing binaries, ∼2200 δ-Scuti variables, and ${\sim }10\, 200$ rotational variables. The light curves and characteristics of the variables are all available through the ASAS-SN variable stars data base (https://asas-sn.osu.edu/variables). The pre-computed ASAS-SN V-band light curves for all the ∼30.1 million sources are available through the ASAS-SN photometry data base (https://asas-sn.osu.edu/photometry). This effort will be extended to provide ASAS-SN light curves for sources in the Northern hemisphere and for V ≲ 17 mag sources across the whole sky that are not included in APASS DR9.
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Abstract We use a multilevel perceptron (MLP) neural network to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The MLP can obtain fairly unbiased photometry for stars from
g ≃ 4 to 14 mag, particularly compared to the dispersion (15%–85% 1σ range around the median) of 0.12 mag for saturated (g < 11.5 mag) stars. More importantly, the light curve of a nonvariable saturated star has a median dispersion of only 0.037 mag. The MLP light curves are, in many cases, spectacularly better than those provided by the standard ASAS-SN pipelines. While the network was trained ong -band data from only one of ASAS-SN’s 20 cameras, initial experiments suggest that it can be used for any camera and the older ASAS-SNV -band data as well. The dominant problems seem to be associated with correctable issues in the ASAS-SN data reduction pipeline for saturated stars more than the MLP itself. The method is publicly available as a light-curve option on ASAS-SN Sky Patrol v1.0. -
ABSTRACT We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than $258\, 000$ APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RR Lyrae, and δ Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3 dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2 dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects.more » « less
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ABSTRACT We catalogue the 443 bright supernovae (SNe) discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) in 2018−2020 along with the 519 SNe recovered by ASAS-SN and 516 additional mpeak ≤ 18 mag SNe missed by ASAS-SN. Our statistical analysis focuses primarily on the 984 SNe discovered or recovered in ASAS-SN g-band observations. The complete sample of 2427 ASAS-SN SNe includes earlier V-band samples and unrecovered SNe. For each SN, we identify the host galaxy, its UV to mid-IR photometry, and the SN’s offset from the centre of the host. Updated peak magnitudes, redshifts, spectral classifications, and host galaxy identifications supersede earlier results. With the increase of the limiting magnitude to g ≤ 18 mag, the ASAS-SN sample is nearly complete up to mpeak = 16.7 mag and is 90 per cent complete for mpeak ≤ 17.0 mag. This is an increase from the V-band sample, where it was roughly complete up to mpeak = 16.2 mag and 70 per cent complete for mpeak ≤ 17.0 mag.
