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 multi-stage 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 Clementini, G; Ripepi, V; Leccia, S ...
Astronomy and astrophysics (Berlin),
11/2016, Letnik:
595
Journal Article
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Context. The European Space Agency spacecraft Gaia is expected to observe about 10000 Galactic Cepheids and over 100000 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.
The WFCAM Science Archive Hambly, N. C.; Collins, R. S.; Cross, N. J. G. ...
Monthly notices of the Royal Astronomical Society,
02/2008, Letnik:
384, Številka:
2
Journal Article
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We describe the WFCAM Science Archive, which is the primary point of access for users of data from the wide-field infrared camera WFCAM on the United Kingdom Infrared Telescope (UKIRT), especially ...science catalogue products from the UKIRT Infrared Deep Sky Survey. We describe the database design with emphasis on those aspects of the system that enable users to fully exploit the survey data sets in a variety of different ways. We give details of the database-driven curation applications that take data from the standard nightly pipeline-processed and calibrated files for the production of science-ready survey data sets. We describe the fundamentals of querying relational databases with a set of astronomy usage examples, and illustrate the results.
Context. In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of deliverables. ...This becomes ever more important as we enter the Gaia era. Aims. We investigate the potential detectability of eclipsing binaries with Gaia using a data set of all Kepler eclipsing binaries sampled with Gaia cadence and folded with the Kepler period. The performance of fitting methods is evaluated in comparison to real Kepler data parameters and a classification scheme is proposed for the potentially detectable sources based on the geometry of the light curve fits. Methods. The polynomial chain (polyfit) and two-Gaussian models are used for light curve fitting of the data set. Classification is performed with a combination of the t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering of applications with noise (DBSCAN) algorithms. Results. We find that ~68% of the Kepler Eclipsing Binary Catalog sources are potentially detectable by Gaia when folded with the Kepler period; we propose a classification scheme of the detectable sources based on the morphological type indicative of the light curve with subclasses that reflect the properties of the fitted model (presence and visibility of eclipses, their width, depth, etc.).
Gaia Data Release 2 Clementini, G.; Ripepi, V.; Molinaro, R. ...
Astronomy and astrophysics (Berlin),
02/2019, Letnik:
622
Journal Article
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Context. The Gaia second Data Release (DR2) presents a first mapping of full-sky RR Lyrae stars and Cepheids observed by the spacecraft during the initial 22 months of science operations. Aims. The ...Specific Objects Study (SOS) pipeline, developed to validate and fully characterise Cepheids and RR Lyrae stars (SOS Cep&RRL) observed by Gaia, has been presented in the documentation and papers accompanying the Gaia first Data Release. Here we describe how the SOS pipeline was modified to allow for processing the Gaia multi-band (G, GBP, and GRP) time-series photometry of all-sky candidate variables and produce specific results for confirmed RR Lyrae stars and Cepheids that are published in the DR2 catalogue. Methods. The SOS Cep&RRL processing uses tools such as the period–amplitude and the period–luminosity relations in the G band. For the analysis of the Gaia DR2 candidates we also used tools based on the GBP and GRP photometry, such as the period–Wesenheit relation in (G, GRP). Results. Multi-band time-series photometry and characterisation by the SOS Cep&RRL pipeline are published in Gaia DR2 for 150 359 such variables (9575 classified as Cepheids and 140 784 as RR Lyrae stars) distributed throughout the sky. The sample includes variables in 87 globular clusters and 14 dwarf galaxies (the Magellanic Clouds, 5 classical and 7 ultra-faint dwarfs). To the best of our knowledge, as of 25 April 2018, the variability of 50 570 of these sources (350 Cepheids and 50 220 RR Lyrae stars) has not been reported before in the literature, therefore they are likely new discoveries by Gaia. An estimate of the interstellar absorption is published for 54 272 fundamental-mode RR Lyrae stars from a relation based on the G-band amplitude and the pulsation period. Metallicities derived from the Fourier parameters of the light curves are also released for 64 932 RR Lyrae stars and 3738 fundamental-mode classical Cepheids with periods shorter than 6.3 days.
Gaia Data Release 2 Lanzafame, A. C.; Distefano, E.; Messina, S. ...
Astronomy and astrophysics (Berlin),
08/2018, Letnik:
616
Journal Article
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Context. Amongst the ≈5 × 105 sources identified as variable stars in Gaia Data Release 2 (DR2), 26% are rotational modulation variable candidates of the BY Dra class. Gaia DR2 provides their ...multi-band (G, GBP, and GRP) photometric time series collected by the European Space Agency spacecraft Gaia during the first 22 months of operations as well as the essential parameters related to their flux modulation induced by surface inhomogeneities and rotation. Aims. We developed methods to identify the BY Dra variable candidates and to infer their variability parameters. Methods. BY Dra candidates were pre-selected from their position in the Hertzsprung–Russel diagram, built from Gaia parallaxes, G magnitudes, and (GBP − GRP) colours. Since the time evolution of the stellar active region can disrupt the coherence of the signal, segments not much longer than their expected evolution timescale were extracted from the entire photometric time series, and period search algorithms were applied to each segment. For the Gaia DR2, we selected sources with similar periods in at least two segments as candidate BY Dra variables. Results were further filtered considering the time-series phase coverage and the expected approximate light-curve shape. Results. Gaia DR2 includes rotational periods and modulation amplitudes of 147 535 BY Dra candidates. The data unveil the existence of two populations with distinctive period and amplitude distributions. The sample covers 38% of the whole sky when divided into bins (HEALPix) of ≈0.84 square degrees, and we estimate that this represents 0.7–5% of all BY Dra stars potentially detectable with Gaia. Conclusions. The preliminary data contained in Gaia DR2 illustrate the vast and unique information that the mission is going to provide on stellar rotation and magnetic activity. This information, complemented by the exquisite Gaia parallaxes, proper motions, and astrophysical parameters, is opening new and unique perspectives for our understanding of the evolution of stellar angular momentum and dynamo action.
Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of ...extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims. We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods. We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed on randomized training sets. Results. We obtain excellent results (about 5% global error rate) with classification into light curve morphology classes on the Hipparcos data. The classification into system morphology classes using the Catalog and Atlas of Eclipsing binaries (CALEB) has a higher error rate (about 10.5%), most importantly due to the (sometimes strong) similarity of the photometric light curves originating from physically different systems. When trained on CALEB and then applied to Kepler-detected eclipsing binaries subsampled according to Gaia observing times, LDA and SOM provide tractable, easy-to-visualize subspaces of the full (functional) space of light curves that summarize the most important phenomenological elements of the individual light curves. The sequence of light curves ordered by their first linear discriminant coefficient is compared to results obtained using local linear embedding. The SOM method proves able to find a 2-dimensional embedded surface in the space of the light curves which separates the system morphology classes in its different regions, and also identifies a few other phenomena, such as the asymmetry of the light curves due to spots, eccentric systems, and systems with a single eclipse. Furthermore, when data from other surveys are projected to the same SOM surface, the resulting map yields a good overview of the general biases and distortions due to differences in time sampling or population.
We present a novel automated methodology to detect and classify periodic variable stars in a large data base of photometric time series. The methods are based on multivariate Bayesian statistics and ...use a multistage approach. We applied our method to the ground-based data of the Trans-Atlantic Exoplanet Survey (TrES) Lyr1 field, which is also observed by the Kepler satellite, covering ∼26 000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, γ Doradus, β Cephei and δ Scuti stars. Also a few classical radial pulsators were found.
Abstract
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.
Abstract
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.
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