2019
DOI: 10.1093/mnras/stz844
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The ASAS-SN Catalog of Variable Stars II: Uniform Classification of 412,000 Known Variables

Abstract: The variable stars in the VSX catalog are derived from a multitude of inhomogeneous data sources and classification tools. This inhomogeneity complicates our understanding of variable star types, statistics, and properties, and it directly affects attempts to build training sets for current (and next) generation all-sky, time-domain surveys. We homogeneously analyze the ASAS-SN V-band light curves of ∼412, 000 variables from the VSX catalog. The variables are classified using an updated random forest classifie… Show more

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Cited by 162 publications
(135 citation statements)
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“…We used the astropy implementation of the Generalized Lomb- Scargle (GLS;Zechmeister & Kürster 2009;Scargle 1982) periodogram and the astrobase implementation (Bhatti et al 2018) of the Box Least Squares (BLS; Kovács et al 2002) periodogram to search for periodicity over the range 0.05 ≤ P ≤ 100 days. Periodic sources were classified using a random forest classifier and quality checks as described in Jayasinghe et al (2019). In order to minimize false positives due to spurious variability signals in the data, we implemented cuts in classification probability Prob > 0.9 and the Lafler-Kinmann string length statistic (Lafler & Kinman 1965;Clarke 2002;Jayasinghe et al 2019) calculated for the magnitudes sorted by phase T (φ|P ) < 0.65.…”
Section: Variability Analysismentioning
confidence: 99%
“…We used the astropy implementation of the Generalized Lomb- Scargle (GLS;Zechmeister & Kürster 2009;Scargle 1982) periodogram and the astrobase implementation (Bhatti et al 2018) of the Box Least Squares (BLS; Kovács et al 2002) periodogram to search for periodicity over the range 0.05 ≤ P ≤ 100 days. Periodic sources were classified using a random forest classifier and quality checks as described in Jayasinghe et al (2019). In order to minimize false positives due to spurious variability signals in the data, we implemented cuts in classification probability Prob > 0.9 and the Lafler-Kinmann string length statistic (Lafler & Kinman 1965;Clarke 2002;Jayasinghe et al 2019) calculated for the magnitudes sorted by phase T (φ|P ) < 0.65.…”
Section: Variability Analysismentioning
confidence: 99%
“…The RF algorithm has been successfully applied to a variety of tasks in astronomy (e.g., Carliles et al 2010;Dubath et al 2011;Richards et al 2012;Carrasco Kind & Brunner 2013;Wyrzykowski et al 2014;Jayasinghe et al 2019;Chen et al 2019), including AGN classification and photometric redshift estimation (e.g., Pichara et al 2012;Carrasco et al 2015;Schindler et al 2017;Nakoneczny et al 2019;Jin et al 2019). We note that our work is the first RF-assisted AGN classification across the whole sky.…”
Section: Random Forest Algorithmmentioning
confidence: 99%
“…Intermediate luminosity optical transients (ILOTs) are exotic outbursts with luminosities which fall between those of novae and supernovae (SN). Many new ILOTs are being discovered by modern surveys and dedicated campaigns (e.g., [1,[7][8][9][10][11][12][28][29][30][36][37][38][39][40][41][42][43][43][44][45][46][47][48][49]51,52,57,58,64,66,70,73]. The group consists of many different astronomical eruptions which are diverse, but are found to have shared properties [32,33,59].…”
Section: Introductionmentioning
confidence: 99%
“…Differently from V838 Mon which is much more energetic, the mass of the destructed planet is too low to cause the YSO to have an inflated envelope, and hence the merger remnant stays hot, and therefore does not redden as the LRNe.In addition to [32] further suggested that another transient, , may also be a related object. The variable low-mass star ASASSN-13db was identified by the All Sky Automated Survey for SuperNovae (ASAS-SN; e.g., [30]), after brightening in the visual by ≈ 4 mag in September 2013 ([27,50,55]). The outburst occurred in an M5 young star surrounded with a proto-planetary disk.…”
mentioning
confidence: 99%