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Register a new userReview of the classification and properties of 62 variable stars in Cygnus
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P. La Rocca
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This study aims to assess the properties and classification of 62 variable stars in Cygnus, little studied since their discovery and originally reported in the Information Bulletin on Variable Stars (IBVS) 1302. Using data from previous studies and several astronomical databases, we performed our analysis mainly utilizing a period analysis software and comparing the photometric characteristics of the variables in a Colour-Absolute Magnitude Diagram. For all stars, the variability is confirmed. We discovered new significant results for the period and/or type of 17 variables and highlighted incorrect cross-reference names on astronomical databases for 23 stars. For 3 stars, whose original type was unknown, we propose a new type. We calculated an epoch of a minimum or a maximum for 24 stars; for 3 of them, the epoch has been defined for the first time. This assessment also identifies some cases for which results from the ASAS-SN Catalog of Variable Stars are not consistent with results from Gaia DR2 and/or our analysis.
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We present a subset of the results of a three season, 124 night, near-infrared monitoring campaign of the dark clouds Lynds 1003 and Lynds 1004 in the Cygnus OB7 star forming region. In this paper, we focus on the field star population. Using three seasons of UKIRT J, H and K band observations spanning 1.5 years, we obtained high-quality photometry on 9,200 stars down to J=17 mag, with photometric uncertainty better than 0.04 mag. After excluding known disk bearing stars we identify 149 variables - 1.6% of the sample. Of these, about 60 are strictly periodic, with periods predominantly < 2 days. We conclude this group is dominated by eclipsing binaries. A few stars have long period signals of between 20 and 60 days. About 25 stars have weak modulated signals, but it was not clear if these were periodic. Some of the stars in this group may be diskless young stellar objects with relatively large variability due to cool star spots. The remaining ~60 stars showed variations which appear to be purely stochastic.
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We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V-I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency. Random forests and a multi-stage scheme involving Bayesian network and Gaussian mixture methods lead to statistically equivalent results. In standard 10-fold cross-validation experiments, the rate of correct classification is between 90 and 100%, depending on the variability type. The main mis-classification cases, up to a rate of about 10%, arise due to confusion between SPB and ACV blue variables and between eclipsing binaries, ellipsoidal variables and other variability types. Our training set and the predicted types for the other Hipparcos periodic stars are available online.
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