Related papers: Photometric light curves classification with machi…

There is an increasing number of large, digital, synoptic sky surveys, in which repeated observations are obtained over large areas of the sky in multiple epochs. Likewise, there is a growth in the number of (often automated or robotic)…

This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The…

Machine Learning methods will play a fundamental role in our ability to optimize the science output from the next generation of large scale surveys. Given the peculiarities of astronomical data, it is crucial that algorithms are adapted to…

With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light…

Within the last years, the classification of variable stars with Machine Learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help…

The Large Synoptic Survey Telescope (LSST) is an ambitious astronomical survey with a similarly ambitious Data Management component. Data Management for LSST includes processing on both nightly and yearly cadences to generate transient…

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference…

The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…

We apply the technique of self-organising maps (Kohonen 1990) to the automated classification of singly periodic astronomical lightcurves. We find that our maps readily distinguish between lightcurve types in both synthetic and real…

This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and…

Microlensing is a powerful tool for discovering cold exoplanets, and the The Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Roman's microlensing events can be used to…

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…

We present an image classification algorithm using deep learning convolutional neural network architecture, which classifies the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with…

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data…

We suggest a new algorithm to remove systematic effects in a large set of lightcurves obtained by a photometric survey. The algorithm can remove systematic effects, like the ones associated with atmospheric extinction, detector efficiency,…

Electronically Assisted Astronomy consists in capturing deep sky images with a digital camera coupled to a telescope to display views of celestial objects that would have been invisible through direct observation. This practice generates a…

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification…

Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector…

Current and future large astronomical surveys will yield multiparameter databases on millions or even billions of objects. The scientific exploitation of these will require powerful, robust, and automated classification tools tailored to…

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…