Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer

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.Comment: 48 pages, 17 figures, 1 table, accepted by A

Non-conservative evolution in Algols: where is the matter?

There is gathering indirect evidence suggesting non-conservative evolutions in Algols. However, the systemic mass-loss rate is poorly constrained by observations and generally set as a free parameter in binary-star evolution simulations. Moreover, systemic mass loss may lead to observational signatures that are still to be found. We investigate the impact of the outflowing gas and the possible presence of dust grains on the spectral energy distribution (SED). We used the 1D plasma code Cloudy and compared the results with the 3D Monte-Carlo radiative transfer code Skirt for dusty simulations. The circumbinary mass-distribution and binary parameters are computed with state-of-the-art binary calculations done with the Binstar evolution code. The outflowing material reduces the continuum flux-level of the stellar SED in the optical and UV. Due to the time-dependence of this effect, it may help to distinguish between different ejection mechanisms. Dust, if present, leads to observable infrared excesses even with low dust-to-gas ratios and traces the cold material at large distances from the star. By searching for such dust emission in the WISE catalogue, we found a small number of Algols showing infrared excesses, among which the two rather surprising objects SX Aur and CZ Vel. We find that some binary B[e] stars show the same strong Balmer continuum as we predict with our models. However, direct evidence of systemic mass loss is probably not observable in genuine Algols, since these systems no longer eject mass through the hotspot mechanism. Furthermore, owing to its high velocity, the outflowing material dissipates in a few hundred years. If hot enough, the hotspot may produce highly ionised species such as SiIV and observable characteristics that are typical of W Ser systems.Comment: Accepted for piblications in A&A; 21 pages, 19 figure

Implementation of a Scientific Subset of Algol 68

Computing and Information Science