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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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Photometry of Saturated Stars with Neural Networks
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 fromg≃ 4 to 14 mag, particularly compared to the dispersion (15%–85% 1σrange 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.
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- PAR ID:
- 10588765
- Publisher / Repository:
- AAS Journals
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 971
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 61
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.3847/1538-4357/ad5a0b
