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
- NSF-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 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 characterize an all-sky catalogue of ∼8400 δ Scuti variables in ASAS-SN, which includes ∼3300 new discoveries. Using distances from Gaia DR2, we derive period–luminosity relationships for both the fundamental mode and overtone pulsators in the WJK, V, Gaia DR2 G, J, H, Ks, and W1 bands. We find that the overtone pulsators have a dominant overtone mode, with many sources pulsating in the second overtone or higher order modes. The fundamental mode pulsators have metallicity-dependent periods, with log10(P) ∼ −1.1 for $\rm [Fe/H]\lt -0.3$ and log10(P) ∼ −0.9 for $\rm [Fe/H]\gt 0$, which leads to a period-dependent scale height. Stars with $P\gt 0.100\, \rm d$ are predominantly located close to the Galactic disc ($\rm |\mathit{ Z}|\lt 0.5\, kpc$). The median period at a scale height of $Z\sim 0\, \rm kpc$ also increases with the Galactocentric radius R, from log10(P) ∼ −0.94 for sources with $R\gt 9\, \rm kpc$ to log10(P) ∼ −0.85 for sources with $R\lt 7\, \rm kpc$, which is indicative of a radial metallicity gradient. To illustrate potential applications of this all-sky catalogue, we obtained 30 min cadence, image subtraction TESS light curves for a sample of 10 fundamental mode and 10 overtone δ Scuti stars discovered by ASAS-SN. From this sample, we identified two new δ Scuti eclipsing binaries, ASASSN-V J071855.62−434247.3 and ASASSN-V J170344.20−615941.2 with short orbital periods of Porb = 2.6096 and 2.5347 d, respectively.more » « less
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Aims. We present a variability-, color-, and morphology-based classifier designed to identify multiple classes of transients and persistently variable and non-variable sources from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST Chilean AGN/Galaxy Evolution Survey (ChANGES). That being said, it also serves as a more general time-domain astronomy study. Methods. The model uses nine colors computed from CatWISE and Pan-STARRS1 (PS1), a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band, since ZTF DR11 treats the light curves observed in a particular combination of field, filter, and charge-coupled device (CCD) quadrant independently. We used a hierarchical local classifier per parent node approach-where each node is composed of a balanced random forest model. We adopted a taxonomy with 17 classes: non-variable stars, non-variable galaxies, three transients (SNIa, SN-other, and CV/Nova), five classes of stochastic variables (lowz-AGN, midz-AGN, highz-AGN, Blazar, and YSO), and seven classes of periodic variables (LPV, EA, EB/EW, DSCT, RRL, CEP, and Periodic-other). Results. The macro-averaged precision, recall, and F1-score are 0.61, 0.75, and 0.62 for the g -band model, and 0.60, 0.74, and 0.61, for the r -band model. When grouping the four AGN classes (lowz-AGN, midz-AGN, highz-AGN, and Blazar) into one single class, its precision-recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. This demonstrates the good performance of the model in classifying AGN candidates. We applied the model to all the sources in the ZTF/4MOST overlapping sky (−28 ≤ Dec ≤ 8.5), avoiding ZTF fields that cover the Galactic bulge (| gal_b | ≤ 9 and gal_l ≤ 50). This area includes 86 576 577 light curves in the g band and 140 409 824 in the r band with 20 or more observations and with an average magnitude in the corresponding band lower than 20.5. Only 0.73% of the g -band light curves and 2.62% of the r -band light curves were classified as stochastic, periodic, or transient with high probability ( P init ≥ 0.9). Even though the metrics obtained for the two models are similar, we find that, in general, more reliable results are obtained when using the g -band model. With it, we identified 384 242 AGN candidates (including low-, mid-, and high-redshift AGN and Blazars), 287 156 of which have P init ≥ 0.9.more » « less
