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Register a new userAutomated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer
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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 assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately +/-2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.
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We have compiled the first all-sky mid-infrared variable-star catalog based on Wide-field Infrared Survey Explorer (WISE) five-year survey data. Requiring more than 100 detections for a given object, 50,282 carefully and robustly selected periodic variables are discovered, of which 34,769 (69%) are new. Most are located in the Galactic plane and near the equatorial poles. A method to classify variables based on their mid-infrared light curves is established using known variable types in the General Catalog of Variable Stars. Careful classification of the new variables results in a tally of 21,427 new EW-type eclipsing binaries, 5654 EA-type eclipsing binaries, 1312 Cepheids, and 1231 RR Lyraes. By comparison with known variables available in the literature, we estimate that the misclassification rate is 5% and 10% for short- and long-period variables, respectively. A detailed comparison of the types, periods, and amplitudes with variables in the Catalina catalog shows that the independently obtained classifications parameters are in excellent agreement. This enlarged sample of variable stars will not only be helpful to study Galactic structure and extinction properties, they can also be used to constrain stellar evolution theory and as potential candidates for the James Webb Space Telescope.
We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. In order to assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if the number of data points is larger than 80 and the duration is more than a few weeks. The classifier software of the subclass model is available from the GitHub repository (https://goo.gl/xmFO6Q).
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 the ground-based data of the TrES Lyr1 field, which is also observed by the Kepler satellite, covering ~26000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, Gamma Doradus, Beta Cephei and Delta Scuti stars. Also a few classical radial pulsators were found.
During the WISE at 5: Legacy and Prospects conference in Pasadena, CA -- which ran from February 10 - 12, 2015 -- attendees were invited to engage in an interactive session exploring the future uses of the Wide-field Infrared Survey Explorer (WISE) data. The 65 participants -- many of whom are extensive users of the data -- brainstormed the top questions still to be answered by the mission, as well as the complementary current and future datasets and additional processing of WISE/NEOWISE data that would aid in addressing these most important scientific questions. The results were mainly bifurcated between topics related to extragalactic studies (e.g. AGN, QSOs) and substellar mass objects. In summary, participants found that complementing WISE/NEOWISE data with cross-correlated multiwavelength surveys (e.g. SDSS, Pan-STARRS, LSST, Gaia, Euclid, etc.) would be highly beneficial for all future mission goals. Moreover, developing or implementing machine-learning tools to comb through and understand cross-correlated data was often mentioned for future uses. Finally, attendees agreed that additional processing of the data such as co-adding WISE and NEOWISE and extracting a multi-epoch photometric database and parallax and proper motion catalog would greatly improve the scientific results of the most important projects identified. In that respect, a project such as MaxWISE which would execute the most important additional processing and extraction as well as make the data and catalogs easily accessible via a public portal was deemed extremely important.
The Wide-Field Infrared Transient Explorer (WINTER) is a new infrared time-domain survey instrument which will be deployed on a dedicated 1 meter robotic telescope at Palomar Observatory. WINTER will perform a seeing-limited time domain survey of the infrared (IR) sky, with a particular emphasis on identifying r-process material in binary neutron star (BNS) merger remnants detected by LIGO. We describe the scientific goals and survey design of the WINTER instrument. With a dedicated trigger and the ability to map the full LIGO O4 positional error contour in the IR to a distance of 190 Mpc within four hours, WINTER will be a powerful kilonova discovery engine and tool for multi-messenger astrophysics investigations. In addition to follow-up observations of merging binaries, WINTER will facilitate a wide range of time-domain astronomical observations, all the while building up a deep coadded image of the static infrared sky suitable for survey science. WINTERs custom camera features six commercial large-format Indium Gallium Arsenide (InGaAs) sensors and a tiled optical system which covers a $>$1-square-degree field of view with 90% fill factor. The instrument observes in Y, J and a short-H (Hs) band tuned to the long-wave cutoff of the InGaAs sensors, covering a wavelength range from 0.9 - 1.7 microns. We present the design of the WINTER instrument and current progress towards final integration at Palomar Observatory and commissioning planned for mid-2021.
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