Maria Süveges

Random forest automated supervised classification of Hipparcos periodic variable stars

We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train supervised algorithms to characterize the type dependencies on a number of attributes. The most useful attributes evaluated with the random forest methodology include, in decreasing order of importance, the period, the amplitude, the V − I colour index, the absolute magnitude, the residual around the folded light-curve model, the magnitude distribution skewness and the amplitude of the second harmonic of the Fourier series model relative to that of the fundamental frequency. Random forests and a multistage scheme involving Bayesian network and Gaussian mixture methods lead to statistically equivalent results. In standard 10-fold cross-validation (CV) experiments, the rate of correct classification is between 90 and 100 per cent, depending on the variability type. The main mis-classification cases, up to a rate of about 10 per cent, arise due to confusion between SPB and ACV blue variables and between eclipsing binaries, ellipsoidal variables and other variability types. Our training set and the predicted types for the other Hipparcos periodic stars are available online.

Gaia Data Release 1 - The Cepheid and RR Lyrae star pipeline and its application to the south ecliptic pole region

Context. The European Space Agency spacecraft Gaia is expected to observe about 10 000 Galactic Cepheids and over 100 000 Milky Way RR Lyrae stars (a large fraction of which will be new discoveries), during the five-year nominal lifetime spent scanning the whole sky to a faint limit of G = 20.7 mag, sampling their light variation on average about 70 times. Aims. We present an overview of the Specific Objects Study (SOS) pipeline developed within the Coordination Unit 7 (CU7) of the Data Processing and Analysis Consortium (DPAC), the coordination unit charged with the processing and analysis of variable sources observed by Gaia , to validate and fully characterise Cepheids and RR Lyrae stars observed by the spacecraft. The algorithms developed to classify and extract information such as the pulsation period, mode of pulsation, mean magnitude, peak-to-peak amplitude of the light variation, subclassification in type, multiplicity, secondary periodicities, and light curve Fourier decomposition parameters, as well as physical parameters such as mass, metallicity, reddening, and age (for classical Cepheids) are briefly described. Methods. The full chain of the CU7 pipeline was run on the time series photometry collected by Gaia during 28 days of ecliptic pole scanning law (EPSL) and over a year of nominal scanning law (NSL), starting from the general Variability Detection, general Characterization, proceeding through the global Classification and ending with the detailed checks and typecasting of the SOS for Cepheids and RR Lyrae stars (SOS Cep&RRL). We describe in more detail how the SOS Cep&RRL pipeline was specifically tailored to analyse Gaia ’s G -band photometric time series with a south ecliptic pole (SEP) footprint, which covers an external region of the Large Magellanic Cloud (LMC), and to produce results for confirmed RR Lyrae stars and Cepheids to be published in Gaia Data Release 1 ( Gaia DR1). Results. G -band time series photometry and characterisation by the SOS Cep&RRL pipeline (mean magnitude and pulsation characteristics) are published in Gaia DR1 for a total sample of 3194 variable stars (599 Cepheids and 2595 RR Lyrae stars), of which 386 (43 Cepheids and 343 RR Lyrae stars) are new discoveries by Gaia . All 3194 stars are distributed over an area extending 38 degrees on either side from a point offset from the centre of the LMC by about 3 degrees to the north and 4 degrees to the east. The vast majority are located within the LMC. The published sample also includes a few bright RR Lyrae stars that trace the outer halo of the Milky Way in front of the LMC.

Automated classification of Hipparcos unsolved variables

We present an automated classification of stars exhibiting periodic, non-periodic and irregular light variations. The Hipparcos catalogue of unsolved variables is employed to complement the training set of periodic variables of Dubath et al. with irregular and non-periodic representatives, leading to 3881 sources in total which describe 24 variability types. The attributes employed to characterize light-curve features are selected according to their relevance for classification. Classifier models are produced with random forests and a multistage methodology based on Bayesian networks, achieving overall misclassification rates under 12 per cent. Both classifiers are applied to predict variability types for 6051 Hipparcos variables associated with uncertain or missing types in the literature.

Search for high-amplitude δ Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis

We propose a robust principal component analysis framework for the exploitation of multiband photometric measurements in large surveys. Period search results are improved using the time-series of the first principal component due to its optimized signal-to-noise ratio. The presence of correlated excess variations in the multivariate time-series enables the detection of weaker variability. Furthermore, the direction of the largest variance differs for certain types of variable stars. This can be used as an efficient attribute for classification. The application of the method to a subsample of Sloan Digital Sky Survey Stripe 82 data yielded 132 high-amplitude δ Scuti variables. We also found 129 new RR Lyrae variables, complementary to the catalogue of Sesar et al., extending the halo area mapped by Stripe 82 RR Lyrae stars towards the Galactic bulge. The sample also comprises 25 multiperiodic or Blazhko RR Lyrae stars.

REVEALING δ CEPHEI’S SECRET COMPANION AND INTRIGUING PAST

Classical Cepheid variable stars are crucial calibrators of the cosmic distance scale thanks to a relation between their pulsation periods and luminosities. Their archetype, {\delta} Cephei, is an important calibrator for this relation. In this paper, we show that {\delta} Cephei is a spectroscopic binary based on newly-obtained high-precision radial velocities. We combine these new data with literature data to determine the orbit, which has period 2201 days, semi-amplitude 1.5 km/s, and high eccentricity (e = 0.647). We re-analyze Hipparcos intermediate astrometric data to measure {\delta} Cepheis parallax (

Gaia Data Release 2: Summary of the variability processing and analysis results

\varpi = 4.09 \pm 0.16

A comparative study of four significance measures for periodicity detection in astronomical surveys

mas) and find tentative evidence for an orbital signature, although we cannot claim detection. We estimate that Gaia will fully determine the astrometric orbit. Using the available information from spectroscopy, velocimetry, astrometry, and Geneva stellar evolution models (

The variable Universe Through the Eyes of Gaia

M_{\delta Cep} ~ 5.0 - 5.25 M_\odot

Gaia eclipsing binary and multiple systems: Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud ⋆

), we constrain the companion mass to within

Investigating light curve modulation via kernel smoothing - I. Application to 53 fundamental mode and first-overtone Cepheids in the LMC

0.2 < M_2 < 1.2 M_\odot