scholarly journals Gaia Data Release 2

2019 ◽  
Vol 625 ◽  
pp. A97 ◽  
Author(s):  
L. Rimoldini ◽  
B. Holl ◽  
M. Audard ◽  
N. Mowlavi ◽  
K. Nienartowicz ◽  
...  
Keyword(s):  
Time Series ◽  
High Amplitude ◽  
Rr Lyrae Stars ◽  
Long Period ◽  
Rr Lyrae ◽  
Multi Stage ◽  
Long Period Variables ◽  
Data Release ◽  
Lyrae Stars

Context. More than half a million of the 1.69 billion sources in Gaia Data Release 2 (DR2) are published with photometric time series that exhibit light variations during the 22 months of observation. Aims. An all-sky classification of common high-amplitude pulsators (Cepheids, long-period variables, δ Scuti/SX Phoenicis, and RR Lyrae stars) is provided for stars with brightness variations greater than 0.1 mag in G band. Methods. A semi-supervised classification approach was employed, firstly training multi-stage random forest classifiers with sources of known types in the literature, followed by a preliminary classification of the Gaia data and a second training phase that included a selection of the first classification results to improve the representation of some classes, before the improved classifiers were applied to the Gaia data. Dedicated validation classifiers were used to reduce the level of contamination in the published results. A relevant fraction of objects were not yet sufficiently sampled for reliable Fourier series decomposition, consequently classifiers were based on features derived from statistics of photometric time series in the G, GBP, and GRP bands, as well as from some astrometric parameters. Results. The published classification results include 195 780 RR Lyrae stars, 150 757 long-period variables, 8550 Cepheids, and 8882 δ Scuti/SX Phoenicis stars. All of these results represent candidates whose completeness and contamination are described as a function of variability type and classification reliability. Results are expressed in terms of class labels and classification scores, which are available in the vari_classifier_result table of the Gaia archive.

scholarly journals Gaia Data Release 2

2018 ◽  
Vol 618 ◽  
pp. A30 ◽  
Author(s):  
B. Holl ◽  
M. Audard ◽  
K. Nienartowicz ◽  
G. Jevardat de Fombelle ◽  
O. Marchal ◽  
...  
Keyword(s):  
Time Series ◽  
Variable Stars ◽  
Great Promise ◽  
Published Data ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Short Timescale ◽  
Classical Statistics ◽  
Data Release ◽  
Lyrae Stars

Context. The Gaia Data Release 2 (DR2) contains more than half a million sources that are identified as variable stars. Aims. We summarise the processing and results of the identification of variable source candidates of RR Lyrae stars, Cepheids, long-period variables (LPVs), rotation modulation (BY Dra-type) stars, δ Scuti and SX Phoenicis stars, and short-timescale variables. In this release we aim to provide useful but not necessarily complete samples of candidates. Methods. The processed Gaia data consist of the G, GBP, and GRP photometry during the first 22 months of operations as well as positions and parallaxes. Various methods from classical statistics, data mining, and time-series analysis were applied and tailored to the specific properties of Gaia data, as were various visualisation tools to interpret the data. Results. The DR2 variability release contains 228 904 RR Lyrae stars, 11 438 Cepheids, 151 761 LPVs, 147 535 stars with rotation modulation, 8882 δ Scuti and SX Phoenicis stars, and 3018 short-timescale variables. These results are distributed over a classification and various Specific Object Studies tables in the Gaia archive, along with the three-band time series and associated statistics for the underlying 550 737 unique sources. We estimate that about half of them are newly identified variables. The variability type completeness varies strongly as a function of sky position as a result of the non-uniform sky coverage and intermediate calibration level of these data. The probabilistic and automated nature of this work implies certain completeness and contamination rates that are quantified so that users can anticipate their effects. Thismeans that even well-known variable sources can be missed or misidentified in the published data. Conclusions. The DR2 variability release only represents a small subset of the processed data. Future releases will include more variable sources and data products; however, DR2 shows the (already) very high quality of the data and great promise for variability studies.

scholarly journals Gaia Data Release 2

2019 ◽  
Vol 622 ◽  
pp. A60 ◽  
Author(s):  
G. Clementini ◽  
V. Ripepi ◽  
R. Molinaro ◽  
A. Garofalo ◽  
T. Muraveva ◽  
...  
Keyword(s):  
Time Series ◽  
Fundamental Mode ◽  
Globular Clusters ◽  
Pulsation Period ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Classical Cepheids ◽  
Data Release ◽  
Lyrae Stars ◽  
Multi Band

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.

scholarly journals Hierarchical Bayesian model to inferPL(Z)relations usingGaiaparallaxes

2019 ◽  
Vol 623 ◽  
pp. A156 ◽  
Author(s):  
H. E. Delgado ◽  
L. M. Sarro ◽  
G. Clementini ◽  
T. Muraveva ◽  
A. Garofalo
Keyword(s):  
Bayesian Model ◽  
Probability Distributions ◽  
Synthetic Data ◽  
Full Description ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Model ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Data Release ◽  
Lyrae Stars

In a recent study we analysed period–luminosity–metallicity (PLZ) relations for RR Lyrae stars using theGaiaData Release 2 (DR2) parallaxes. It built on a previous work that was based on the firstGaiaData Release (DR1), and also included period–luminosity (PL) relations for Cepheids and RR Lyrae stars. The method used to infer the relations fromGaiaDR2 data and one of the methods used forGaiaDR1 data was based on a Bayesian model, the full description of which was deferred to a subsequent publication. This paper presents the Bayesian method for the inference of the parameters ofPL(Z) relations used in those studies, the main feature of which is to manage the uncertainties on observables in a rigorous and well-founded way. The method encodes the probability relationships between the variables of the problem in a hierarchical Bayesian model and infers the posterior probability distributions of thePL(Z) relationship coefficients using Markov chain Monte Carlo simulation techniques. We evaluate the method with several semi-synthetic data sets and apply it to a sample of 200 fundamental and first-overtone RR Lyrae stars for whichGaiaDR1 parallaxes and literatureKs-band mean magnitudes are available. We define and test several hyperprior probabilities to verify their adequacy and check the sensitivity of the solution with respect to the prior choice. The main conclusion of this work, based on the test with semi-syntheticGaiaDR1 parallaxes, is the absolute necessity of incorporating the existing correlations between the period, metallicity, and parallax measurements in the form of model priors in order to avoid systematically biased results, especially in the case of non-negligible uncertainties in the parallaxes. The relation coefficients obtained here have been superseded by those presented in our recent paper that incorporates the findings of this work and the more recentGaiaDR2 measurements.

scholarly journals The long-period field RR Lyrae stars

1990 ◽  
Vol 360 ◽  
pp. 604 ◽  
Author(s):  
Edward G. Schmidt ◽  
Charles G. Loomis ◽  
Andrew T. Groebner ◽  
Chris T. Potter
Keyword(s):  
Rr Lyrae Stars ◽  
Long Period ◽  
Rr Lyrae ◽  
Lyrae Stars

STRucture and Evolution of the GAlaxy (STREGA): The case of Pal 3

2019 ◽  
Vol 14 (S351) ◽  
pp. 478-481
Author(s):  
M. I. Moretti ◽  
I. Musella ◽  
M. Marconi ◽  
V. Ripepi ◽  
R. Molinaro
Keyword(s):  
Time Series ◽  
Globular Cluster ◽  
Variable Stars ◽  
Rr Lyrae Stars ◽  
North East ◽  
The North ◽  
Rr Lyrae ◽  
The Galaxy ◽  
Lyrae Stars ◽  
Candidate Variable

AbstractIn the context of the STRucture and Evolution of the GAlaxy survey, we describe the preliminary results obtained for the fields around the globular cluster Pal 3 (about 2.75 square degrees), by exploiting the obtained g, r, i time series photometry. The final aim is to use variable stars as tools to verify and study the presence of streams around Pal 3. We found 20 candidate variable stars of which 7 RR Lyrae stars possibly belonging to Pal 3, also at large distance from the center. The distribution of the candidate RR Lyrae seems to confirm a preferential distribution in the north-east direction, confirming previous results in literature.

scholarly journals A DECam view of the diffuse dwarf galaxy Crater II – Variable stars

2019 ◽  
Vol 492 (1) ◽  
pp. 1061-1077 ◽  
Author(s):  
A Katherina Vivas ◽  
Alistair R Walker ◽  
Clara E Martínez-Vázquez ◽  
Matteo Monelli ◽  
Giuseppe Bono ◽  
...  
Keyword(s):  
Time Series ◽  
Dwarf Galaxy ◽  
Position Angle ◽  
Variable Stars ◽  
Distance Modulus ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
The Galaxy ◽  
Binary Evolution ◽  
Lyrae Stars

ABSTRACT Time series observations of a single dithered field centred on the diffuse dwarf satellite galaxy Crater II were obtained with the Dark Energy Camera (DECam) at the 4m Blanco Telescope at Cerro Tololo Inter-American Observatory, Chile, uniformly covering up to two half-light radii. Analysis of the g and i time series results in the identification and characterization of 130 periodic variable stars, including 98 RR Lyrae stars, 7 anomalous Cepheids, and 1 SX Phoenicis star belonging to the Crater II population, and 24 foreground variables of different types. Using the large number of ab-type RR Lyrae stars present in the galaxy, we obtained a distance modulus to Crater II of (m − M)0 = 20.333 ± 0.004 (stat) ±0.07 (sys). The distribution of the RR Lyrae stars suggests an elliptical shape for Crater II, with an ellipticity of 0.24 and a position angle of 153°. From the RR Lyrae stars, we infer a small metallicity dispersion for the old population of Crater II of only 0.17 dex. There are hints that the most metal-poor stars in that narrow distribution have a wider distribution across the galaxy, while the slightly more metal-rich part of the population is more centrally concentrated. Given the features in the colour–magnitude diagram of Crater II, the anomalous Cepheids in this galaxy must have formed through a binary evolution channel of an old population.

scholarly journals Local RR Lyrae stars: native and alien

2019 ◽  
Vol 492 (2) ◽  
pp. 2161-2176 ◽  
Author(s):  
R Zinn ◽  
X Chen ◽  
A C Layden ◽  
D I Casetti-Dinescu
Keyword(s):  
Milky Way ◽  
Large Magellanic Cloud ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Angular Momenta ◽  
Data Release ◽  
Total Energies ◽  
Using Data ◽  
Lyrae Stars ◽  
Existing Data

ABSTRACT Measurements of [Fe/H] and radial velocity are presented for 89 RR Lyrae (RRL) candidates within 6 kpc of the Sun. After the removal of two suspected non-RRLs, these stars were added to an existing data base, which yielded 464 RRLs with [Fe/H] on a homogeneous scale. Using data from the Gaia satellite (Data Release 2), we calculated the positions and space velocities for this sample. These data confirm the existence of a thin disc of RRL with [α/Fe] ∼ solar. The majority of the halo RRLs with large total energies have near-zero angular momenta about the Z-axis. Kinematically, these stars closely resemble the Gaia-Sausage/Gaia-Enceladus stars that others have proposed are debris from the merger of a large galaxy with the Milky Way. The metallicity and period distributions of the RRLs and their positions in the period–amplitude diagram suggest that this disrupted galaxy was as massive as the Large Magellanic Cloud and possibly greater.

scholarly journals Informative Bayesian model selection for RR Lyrae star classifiers

2021 ◽  
Vol 503 (1) ◽  
pp. 484-497
Author(s):  
F Pérez-Galarce ◽  
K Pichara ◽  
P Huijse ◽  
M Catelan ◽  
D Mery
Keyword(s):  
Machine Learning ◽  
Marginal Likelihood ◽  
Likelihood Estimation ◽  
Variable Stars ◽  
Training Data ◽  
Multiple Sources ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Lyrae Stars

ABSTRACT Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several astronomical catalogues. However, some scientific articles have also shown that the training data therein contain multiple sources of bias. Hence, the performance of those classifiers on objects not belonging to the training data is uncertain, potentially resulting in the selection of incorrect models. Besides, it gives rise to the deployment of misleading classifiers. An example of the latter is the creation of open-source labelled catalogues with biased predictions. In this paper, we develop a method based on an informative marginal likelihood to evaluate variable star classifiers. We collect deterministic rules that are based on physical descriptors of RR Lyrae stars, and then, to mitigate the biases, we introduce those rules into the marginal likelihood estimation. We perform experiments with a set of Bayesian logistic regressions, which are trained to classify RR Lyraes, and we found that our method outperforms traditional non-informative cross-validation strategies, even when penalized models are assessed. Our methodology provides a more rigorous alternative to assess machine learning models using astronomical knowledge. From this approach, applications to other classes of variable stars and algorithmic improvements can be developed.

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