Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia

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 imminent as we enter the Gaia era. 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 with 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. 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-SNE (t-distrubuted Stochastic Neighbor Embedding) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. We find that approximately 68% of Kepler Eclipsing Binary sources are potentially detectable by Gaia when folded with the Kepler period and 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.).Comment: 9 pages, 18 figures, accepted for publication in Astronomy & Astrophysic

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 $\chi^2$. 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&

Position estimation for a mobile robot using monocular vision and odometry

We describe a localisation system for a robot moving in a known environment . Unlike the currently used methods for industrial robots, our approach does not require any beacons to be installed : the system uses odometry to estimate the vehicle position continuously, and corrects this estimation when necessary by identifying some objects of the environment through vision . These objects, used as landmarks, were previously recorded in a data base . The different parts of the system are presented particularly the way the uncertainty on odometry is updated and how prior knowledge (position estimation and data base) is employed to facilitate landmark identification. 7 cm on xy and I deg on the heading is the typical precision obtained in term of localisation .Nous présentons un système de localisation pour un robot mobile évoluant dans un environnement connu. La méthode, contrairement à celles actuellement utilisées dans l'industrie, ne nécessite pas l'équipement du site en balises : la position du robot est estimée à chaque instant par odométrie, et recalée périodiquement en repérant, à l'aide d'une caméra mobile montée sur le véhicule, des objets de l'environnement jouant le rôle d'amer. Ces objets sont répertoriés dans une base de données constituée au préalable. Les différentes composantes du système sont présentées : nous montrons en particulier comment l'incertitude sur la position du robot évolue avec les erreurs d'odométrie, et comment les connaissances a priori (position estimée, base de données) sont mises à profit pour identifier les amers. La précision typiquement obtenue en matière de localisation est de 7 cm selon xy et 1 deg en cap

Gaia Focused Product Release:Radial velocity time series of long-period variables

Context. The third Gaia Data Release (DR3) provided photometric time series of more than 2 million long-period variable (LPV) candidates. Anticipating the publication of full radial-velocity data planned with Data Release 4, this Focused Product Release (FPR) provides radial-velocity time series for a selection of LPV candidates with high-quality observations.Aims. We describe the production and content of the Gaia catalog of LPV radial-velocity time series, and the methods used to compute the variability parameters published as part of the Gaia FPR.Methods. Starting from the DR3 catalog of LPV candidates, we applied several filters to construct a sample of sources with high-quality radial-velocity measurements. We modeled their radial-velocity and photometric time series to derive their periods and amplitudes, and further refined the sample by requiring compatibility between the radial-velocity period and at least one of the G, GBP, or GRP photometric periods.Results. The catalog includes radial-velocity time series and variability parameters for 9614 sources in the magnitude range 6 ≲ G/mag ≲ 14, including a flagged top-quality subsample of 6093 stars whose radial-velocity periods are fully compatible with the values derived from the G, GBP, and GRP photometric time series. The radial-velocity time series contain a mean of 24 measurements per source taken unevenly over a duration of about three years. We identify the great majority of the sources (88%) as genuine LPV candidates, with about half of them showing a pulsation period and the other half displaying a long secondary period. The remaining 12% of the catalog consists of candidate ellipsoidal binaries. Quality checks against radial velocities available in the literature show excellent agreement. We provide some illustrative examples and cautionary remarks.Conclusions. The publication of radial-velocity time series for almost ten thousand LPV candidates constitutes, by far, the largest such database available to date in the literature. The availability of simultaneous photometric measurements gives a unique added value to the Gaia catalog

Mesoscale magnetism at the grain boundaries in colossal magnetoresistive films

We report the discovery of mesoscale regions with distinctive magnetic properties in epitaxial La$_{1-x}$Sr$_{x}$MnO$_{3}$ films which exhibit tunneling-like magnetoresistance across grain boundaries. By using temperature-dependent magnetic force microscopy we observe that the mesoscale regions are formed near the grain boundaries and have a different Curie temperature (up to 20 K {\it higher}) than the grain interiors. Our images provide direct evidence for previous speculations that the grain boundaries in thin films are not magnetically and electronically sharp interfaces. The size of the mesoscale regions varies with temperature and nature of the underlying defect.Comment: 4 pages of text, 4 figure

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 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 onlin

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