Related papers: Supervised Ensemble Classification of Kepler Varia…

Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…

A recent analysis of high precision photometry obtained using the Kepler spacecraft has revealed two surprising discoveries: (1) over 860 main sequence A-type stars -- approximately 40% of those identified in the Kepler field -- exhibit…

With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. (2016) recently introduced a new method based on machine learning for…

A few percent of all stars are variable, yet over 90% of variables brighter than 12 magnitude have not been discovered yet. There is a need for an all sky search and for the early detection of any unexpected events: optical flashes from…

With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a classification procedure for about 1700 stars…

We present the first analysis of results from the SuperWASP Variable Stars Zooniverse project, which is aiming to classify 1.6 million phase-folded light curves of candidate stellar variables observed by the SuperWASP all sky survey with…

The ~ 200,000 stars observed by the Kepler mission have provided unprecedented constraints across astrophysics. With the advent of modern spectroscopic and photometric surveys, new limits in stellar characterizations are within reach. In…

Robust fast methods to classify variable light curves in large sky surveys are becoming increasingly important. While it is relatively straightforward to identify common periodic stars and particular transient events (supernovae, novae,…

We investigate the consistency of visual morphological classifications of galaxies by comparing classifications for 831 galaxies from six independent observers. The galaxies were classified on laser print copy images or on computer screen…

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to…

We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will…

We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable…

The number of known periodic variable stars has increased rapidly in recent years. As an all-sky transit survey, the Transiting Exoplanet Survey Satellite (TESS) plays an important role in detecting low-amplitude variable stars. Using…

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning…

During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are…

The Kepler Mission began its 3.5-year photometric monitoring campaign in May 2009 on a select group of approximately 150,000 stars. The stars were chosen from the ~half million in the field of view that are brighter than 16th magnitude. The…

The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. It is important to evaluate potential existing tools and determine those that are optimal for extracting scientific…

The discovery of habitable exoplanets has long been a heated topic in astronomy. Traditional methods for exoplanet identification include the wobble method, direct imaging, gravitational microlensing, etc., which not only require a…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

The Gaia mission has observed over 2 billion stars repeatedly across the entire sky over 10 years, revealing the many astronomical objects that vary on human timescales from seconds to years. Its repeated astrometric, photometric,…