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Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected systematic errors limit the practical applicability of this approach to high-amplitude variability and well-behaving data sets. Searching for a new variability detection technique that would be applicable to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties, we propose to consider variability detection as a classification problem that can be approached with machine learning. We compare several classification algorithms: Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects identified) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, 13 low-amplitude variables are new discoveries. We find that the considered machine learning classifiers are more efficient (they find more variables and less false candidates) compared to traditional techniques that consider individual variability indices or their linear combination. The NN, SGB, SVM and RF show a higher efficiency compared to LR and kNN.
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Carbon stars and DZ white dwarfs are two types of rare objects in the Galaxy. In this paper, we have applied the label propagation algorithm to search for these two types of stars from Data Release Eight (DR8) of the Sloan Digital Sky Survey (SDSS), which is verified to be efficient by calculating precision and recall. From nearly two million spectra including stars, galaxies and QSOs, we have found 260 new carbon stars in which 96 stars have been identified as dwarfs and 7 identified as giants, and 11 composition spectrum systems (each of them consists of a white dwarf and a carbon star). Similarly, using the label propagation method, we have obtained 29 new DZ white dwarfs from SDSS DR8. Compared with PCA reconstructed spectra, the 29 findings are typical DZ white dwarfs. We have also investigated their proper motions by comparing them with proper motion distribution of 9,374 white dwarfs, and found that they satisfy the current observed white dwarfs by SDSS generally have large proper motions. In addition, we have estimated their effective temperatures by fitting the polynomial relationship between effective temperature and g-r color of known DZ white dwarfs, and found 12 of the 29 new DZ white dwarfs are cool, in which nine are between 6000K and 6600K, and three are below 6000K.
With recent developments in imaging and computer technology the amount of available astronomical data has increased dramatically. Although most of these data sets are not dedicated to the study of variable stars much of it can, with the application of proper software tools, be recycled for the discovery of new variable stars. Fits Viewer and Data Retrieval System is a new software package that takes advantage of modern computer advances to search astronomical data for new variable stars. More than 200 new variable stars have been found in a data set taken with the Calvin College Rehoboth Robotic telescope using FVDRS. One particularly interesting example is a very fast subdwarf B with a 95 minute orbital period, the fastest currently known of the HW Vir type.
Having a need to perform differential photometry for tens of thousands stars in a several square degrees field, we developed Astrokit program. The software corrects the star brightness variations caused by variations of atmospheric transparency: to this end, the program selects for each star an individual ensemble of reference stars having similar magnitudes and positions in the frame. With ten or more reference stars in the ensemble, the differences between their spectral types and the spectral type of the object studied become unimportant. Astrokit searches for variable stars using Robust Median Statistics criterion, which allows candidate variables to be selected more efficiently than by analyzing the standard deviation of star magnitudes. The software allows very precise automatic analysis of long inhomogeneous sets of photometric observations of a large number of objects to be performed, making it possible to find hot Jupiter type exoplanet transits and low-amplitude variables. We describe the algorithm of the program and the results of its application to reduce the data of the photometric sky survey in Cygnus as well as observations of the open cluster NGC188 and the transit of the exoplanet WASP-11 b / HAT-P-10 b, performed with the MASTER-II-URAL telescope of the Kourovka Astronomical Observatory of the Ural Federal University.
By now, tens of gravitational-wave (GW) events have been detected by the LIGO and Virgo detectors. These GWs have all been emitted by compact binary coalescence, for which we have excellent predictive models. However, there might be other sources for which we do not have reliable models. Some are expected to exist but to be very rare (e.g., supernovae), while others may be totally unanticipated. So far, no unmodeled sources have been discovered, but the lack of models makes the search for such sources much more difficult and less sensitive. We present here a search for unmodeled GW signals using semi-supervised machine learning. We apply deep learning and outlier detection algorithms to labeled spectrograms of GW strain data, and then search for spectrograms with anomalous patterns in public LIGO data. We searched $sim 13%$ of the coincident data from the first two observing runs. No candidates of GW signals were detected in the data analyzed. We evaluate the sensitivity of the search using simulated signals, we show that this search can detect spectrograms containing unusual or unexpected GW patterns, and we report the waveforms and amplitudes for which a $50%$ detection rate is achieved.
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data describing 11 types of variable stars from the Catalina Real-Time Transient Surveys (CRTS), we illustrate how to capture the most important information from computed features and describe detailed methods of how to robustly use Information Theory for feature selection and evaluation. We apply three Machine Learning (ML) algorithms and demonstrate how to optimize these classifiers via cross-validation techniques. For the CRTS dataset, we find that the Random Forest (RF) classifier performs best in terms of balanced-accuracy and geometric means. We demonstrate substantially improved classification results by converting the multi-class problem into a binary classification task, achieving a balanced-accuracy rate of $sim$99 per cent for the classification of ${delta}$-Scuti and Anomalous Cepheids (ACEP). Additionally, we describe how classification performance can be improved via converting a flat-multi-class problem into a hierarchical taxonomy. We develop a new hierarchical structure and propose a new set of classification features, enabling the accurate identification of subtypes of cepheids, RR Lyrae and eclipsing binary stars in CRTS data.
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