27 research outputs found
Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud
The advent of large scale multi-epoch surveys raises the need for automated
light curve (LC) processing. This is particularly true for eclipsing binaries
(EBs), which form one of the most populated types of variable objects. The Gaia
mission, launched at the end of 2013, is expected to detect of the order of few
million EBs over a 5-year mission.
We present an automated procedure to characterize EBs based on the geometric
morphology of their LCs with two aims: first to study an ensemble of EBs on a
statistical ground without the need to model the binary system, and second to
enable the automated identification of EBs that display atypical LCs. We model
the folded LC geometry of EBs using up to two Gaussian functions for the
eclipses and a cosine function for any ellipsoidal-like variability that may be
present between the eclipses. The procedure is applied to the OGLE-III data set
of EBs in the Large Magellanic Cloud (LMC) as a proof of concept. The bayesian
information criterion is used to select the best model among models containing
various combinations of those components, as well as to estimate the
significance of the components.
Based on the two-Gaussian models, EBs with atypical LC geometries are
successfully identified in two diagrams, using the Abbe values of the original
and residual folded LCs, and the reduced . Cleaning the data set from
the atypical cases and further filtering out LCs that contain non-significant
eclipse candidates, the ensemble of EBs can be studied on a statistical ground
using the two-Gaussian model parameters. For illustration purposes, we present
the distribution of projected eccentricities as a function of orbital period
for the OGLE-III set of EBs in the LMC, as well as the distribution of their
primary versus secondary eclipse widths.Comment: 20 pages, 29 figures. Submitted to A&
Gaia Data Release 2: Short-timescale variability processing and analysis
The Gaia DR2 sample of short-timescale variable candidates results from the
investigation of the first 22 months of Gaia photometry for a subsample of
sources at the Gaia faint end. For this exercise, we limited ourselves to the
case of suspected rapid periodic variability. Our study combines
fast-variability detection through variogram analysis, high-frequency search by
means of least-squares periodograms, and empirical selection based on the
investigation of specific sources seen through the Gaia eyes (e.g. known
variables or visually identified objects with peculiar features in their light
curves). The progressive definition and validation of this selection criterion
also benefited from supplementary ground-based photometric monitoring of a few
preliminary candidates, performed at the Flemish Mercator telescope (Canary
Islands, Spain) between August and November 2017. We publish a list of 3,018
short-timescale variable candidates, spread throughout the sky, with a
false-positive rate up to 10-20% in the Magellanic Clouds, and a more
significant but justifiable contamination from longer-period variables between
19% and 50%, depending on the area of the sky. Although its completeness is
limited to about 0.05%, this first sample of Gaia short-timescale variables
recovers some very interesting known short-period variables, such as
post-common envelope binaries or cataclysmic variables, and brings to light
some fascinating, newly discovered variable sources. In the perspective of
future Gaia data releases, several improvements of the short-timescale
variability processing are considered, by enhancing the existing variogram and
period-search algorithms or by classifying the identified candidates.
Nonetheless, the encouraging outcome of our Gaia DR2 analysis demonstrates the
power of this mission for such fast-variability studies, and opens great
perspectives for this domain of astrophysics
Hipparcos Variable Star Detection and Classification Efficiency
A complete periodic star extraction and classification scheme is set up and
tested with the Hipparcos catalogue. The efficiency of each step is derived by
comparing the results with prior knowledge coming from the catalogue or from
the literature. A combination of two variability criteria is applied in the
first step to select 17 006 variability candidates from a complete sample of
115 152 stars. Our candidate sample turns out to include 10 406 known variables
(i.e., 90% of the total of 11 597) and 6600 contaminating constant stars. A
random forest classification is used in the second step to extract 1881 (82%)
of the known periodic objects while removing entirely constant stars from the
sample and limiting the contamination of non-periodic variables to 152 stars
(7.5%). The confusion introduced by these 152 non-periodic variables is
evaluated in the third step using the results of the Hipparcos periodic star
classification presented in a previous study (Dubath et al. [1]).Comment: 8 pages, 7 figure
Gaia Data Release 3: The first Gaia catalogue of variable AGN
One of the novelties of the Gaia-DR3 with respect to the previous data
releases is the publication of the multiband light curves of about 1 million
AGN. The goal of this work was the creation of a catalogue of variable AGN,
whose selection was based on Gaia data only. We first present the
implementation of the methods to estimate the variability parameters into a
specific object study module for AGN. Then we describe the selection procedure
that led to the definition of the high-purity variable AGN sample and analyse
the properties of the selected sources. We started from a sample of millions of
sources, which were identified as AGN candidates by 11 different classifiers
based on variability processing. Because the focus was on the variability
properties, we first defined some pre-requisites in terms of number of data
points and mandatory variability parameters. Then a series of filters was
applied using only Gaia data and the Gaia Celestial Reference Frame 3
(Gaia-CRF3) sample as a reference.The resulting Gaia AGN variable sample, named
GLEAN, contains about 872000 objects, more than 21000 of which are new
identifications. We checked the presence of contaminants by cross-matching the
selected sources with a variety of galaxies and stellar catalogues. The
completeness of GLEAN with respect to the variable AGN in the last Sloan
Digital Sky Survey quasar catalogue is about 47%, while that based on the
variable AGN of the Gaia-CRF3 sample is around 51%. From both a comparison with
other AGN catalogues and an investigation of possible contaminants, we conclude
that purity can be expected to be above 95%. Multiwavelength properties of
these sources are investigated. In particular, we estimate that about 4% of
them are radio-loud. We finally explore the possibility to evaluate the time
lags between the flux variations of the multiple images of strongly lensed
quasars, and show one case.Comment: 19 pages, 31 figures, 2 table. This paper is part of Gaia Data
Release 3 (DR3). In press for A&
Long Period Variables in the Large Magellanic Cloud from the EROS-2 survey
Context. The EROS-2 survey has produced a database of millions of time series
from stars monitored for more than six years, allowing to classify some of
their sources into different variable star types. Among these, Long Period
Variables (LPVs), known to follow sequences in the period-luminosity diagram,
include long secondary period variables whose variability origin is still a
matter of debate.
Aims.We use the 856 864 variable stars available from the Large Magellanic
Cloud (LMC) in the EROS-2 database to detect, classify and characterize LPVs.
Methods. Our method to extract LPVs is based on the statistical Abbe test. It
investigates the regularity of the light curve with respect to the survey
duration in order to extract candidates with long-term variability. The period
search is done by Deeming, Lomb-Scargle and generalized Lomb-Scargle methods,
combined with Fourier series fit. Color-magnitude, period-magnitude and
period-amplitude diagrams are used to characterize our candidates.
Results. We present a catalog of 43 551 LPV candidates for the Large
Magellanic Cloud. For each of them, we provide up to five periods, mean
magnitude in EROS-2, 2MASS and Spitzer bands, BE-RE color, RE amplitude and
spectral type.We use infrared data to make the distinction between RGB, O-rich,
C-rich and extreme AGB stars. Properties of our LPV candidates are investigated
by analyzing period-luminosity and period-amplitude diagrams.Comment: Accepted for publication in A&
Gaia Data Release 2: All-sky classification of high-amplitude pulsating stars
Out of the 1.69 billion sources in the Gaia Data Release 2 (DR2), more than half a million are published with photometric time series that exhibit light variations during 22 months of observation. An all-sky classification of common high-amplitude pulsators (Cepheids, long-period variables, Delta Scuti / SX Phoenicis, and RR Lyrae stars) is provided for stars with brightness variations greater than 0.1 mag in the G band. 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 application of the improved classifiers 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, so classifiers were based on features derived from statistics of photometric time series in the G, BP, and RP bands, as well as from some astrometric parameters. The published classification results include 195,780 RR Lyrae stars, 150,757 long-period variables, 8550 Cepheids, and 8882 Delta 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
Gaia Data Release 2. All-sky classification of high-amplitude pulsating stars
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. An all-sky classification of common
high-amplitude pulsators (Cepheids, long-period variables, Delta Scuti / SX
Phoenicis, and RR Lyrae stars) is provided for stars with brightness variations
greater than 0.1 mag in G band. 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, BP, and RP bands, as well as from some astrometric parameters. The
published classification results include 195,780 RR Lyrae stars, 150,757
long-period variables, 8550 Cepheids, and 8882 Delta 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.Comment: 21 pages, 33 figures, with minor revisions, in press (Astronomy &
Astrophysics
Modelling Long-Period Variables in the Gaia Era
Luminous red giant stars exhibit variability due to stellar pulsation, that is interconnected with uncertain processes (convection, mass loss and dust formation) and results in observable features that are strongly related to stellar properties. These so-called long-period variables (LPVs) provide us with a powerful tool to infer global stellar parameters and constrain the physics of late evolutionary phases in intermediate- and old-age stellar populations. Moreover, their period-luminosity relations represent a highly promising distance indicator. Large-scale optical microlensing surveys carried out during the last few decades made ideal laboratories out of the Magellanic Clouds to investigate the ensemble properties of LPVs with low impact from distance and interstellar extinction. Building on those results, the second data release (DR2) from the Gaia mission is providing new insight on these objects and novel methods to exploit them in the study of the evolution of stars and stellar populations. These results, together with related developments, are summarized here...