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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.
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 s
The All-Sky Automated Survey for Supernovae (ASAS-SN) provides long baseline (${sim}4$ yrs) $V-$band light curves for sources brighter than V$lesssim17$ mag across the whole sky. We produced V-band light curves for a total of ${sim}61.5$ million sour
We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and use a multi-stage approach. We applied our method to
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 algorit
We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star c