A Package for the Automated Classification of Periodic Variable Stars

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).Comment: 16 pages, 11 figures, accepted for publication in A&

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

Near-Ultraviolet Properties of a Large Sample of Type Ia Supernovae as Observed with the Swift UVOT

We present ultraviolet (UV) and optical photometry of 26 Type Ia supernovae (SNe~Ia) observed from March 2005 to March 2008 with the NASA {\it Swift} Ultraviolet and Optical Telescope (UVOT). The dataset consists of 2133 individual observations, making it by far the most complete study of the UV emission from SNe~Ia to date. Grouping the SNe into three subclasses as derived from optical observations, we investigate the evolution of the colors of these SNe, finding a high degree of homogeneity within the normal subclass, but dramatic differences between that group and the subluminous and SN 2002cx-like groups. For the normal events, the redder UV filters on UVOT ($u$, $uvw1$) show more homogeneity than do the bluer UV filters ($uvm2$, $uvw2$). Searching for purely UV characteristics to determine existing optically based groupings, we find the peak width to be a poor discriminant, but we do see a variation in the time delay between peak emission and the late, flat phase of the light curves. The UV light curves peak a few days before the $B$ band for most subclasses (as was previously reported by Jha et al. 2006a), although the SN 2002cx-like objects peak at a very early epoch in the UV. That group also features the bluest emission observed among SNe~Ia. As the observational campaign is ongoing, we discuss the critical times to observe, as determined by this study, in order to maximize the scientific output of future observations.Comment: Accepted to Astrophysical Journa

XTE J1701-462 and its Implications for the Nature of Subclasses in Low-Magnetic-Field Neutron Star Low-Mass X-Ray Binaries

We report on an analysis of RXTE data of the transient neutron star low-mass X-ray binary (NS-LMXB) XTE J1701-462, obtained during its 2006-2007 outburst. The X-ray properties of the source changed between those of various types of NS-LMXB subclasses. At high luminosities the source switched between two types of Z source behavior and at low luminosities we observed a transition from Z source to atoll source behavior. These transitions between subclasses primarily manifest themselves as changes in the shapes of the tracks in X-ray color-color and hardness-intensity diagrams, but they are accompanied by changes in the kHz quasi-periodic oscillations, broad-band variability, burst behavior, and/or X-ray spectra. We find that the low-energy X-ray flux is a good parameter to track the gradual evolution of the tracks in color-color and hardness-intensity diagrams, allowing us to resolve the evolution of the source in greater detail than before and relate the observed properties to other NS-LMXBs. We further find that during the transition from Z to atoll, characteristic behavior known as the atoll upper banana can equivalently be described as the final stage of a weakening Z source flaring branch, thereby blurring the line between the two subclasses. Our findings strongly suggest that the wide variety in behavior observed in NS-LXMBs with different luminosities can be linked through changes in a single variable parameter, namely the mass accretion rate, without the need for additional differences in the neutron star parameters or viewing angle. We briefly discuss the implications of our findings for the spectral changes observed in NS LMXBs and suggest that, contrary to what is often assumed, the position along the color-color tracks of Z sources is not determined by the instantaneous mass accretion rate.Comment: Submitted to ApJ, comments are welcome. 13 pages, 8 figure

Interpretable detection of unstable smart TV usage from power state logs

Power state logs from smart TVs are collected in order to construct a time-series representation of their usage. Time-series that belong to a TV exhibiting instability problems are classified accordingly. To do so, an automated feature extraction approach is used, together with linear classification methods in order to realise interpretable classification decisions. A normalized true positive rate of 0.84 ± 0.10 is obtained for the classification. The normalized true negative rate equals 0.80 ± 0.03. The final model returns a regularity statistic called the Approximate Entropy as its most important feature

Anisotropic selection in cellular genetic algorithms

In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme