Minia Manteiga

The Gaia astrophysical parameters inference system (Apsis) - Pre-launch description

The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Its main objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaias unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellites data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (RMS residuals) on F,G,K,M dwarfs and giants at G=15 (V=15-17) for a wide range of metallicites and interstellar extinctions of around 100K in effective temperature (Teff), 0.1mag in extinction (A0), 0.2dex in metallicity ([Fe/H]), and 0.25dex in surface gravity (logg). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in November 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data.

CONSTRAINING THE EVOLUTION OF ZZ CETI

We report our analysis of the stability of pulsation periods in the DAV star (pulsating hydrogen atmosphere white dwarf) ZZ Ceti, also called R548. On the basis of observations that span 31 years, we conclude that the period 213.13 s observed in ZZ Ceti drifts at a rate dP/dt ≤ (5.5 ± 1.9) × 10-15 s s-1, after correcting for proper motion. Our results are consistent with previous values for this mode and an improvement over them because of the larger time base. The characteristic stability timescale implied for the pulsation period is P/ ≥ 1.2 Gyr, comparable to the theoretical cooling timescale for the star. Our current stability limit for the period 213.13 s is only slightly less than the present measurement for another DAV, G117-B15A, for the period 215.2 s, establishing this mode in ZZ Ceti as the second most stable optical clock known, comparable to atomic clocks and more stable than most pulsars. Constraining the cooling rate of ZZ Ceti aids theoretical evolutionary models and white dwarf cosmochronology. The drift rate of this clock is small enough that we can set interesting limits on reflex motion due to planetary companions.

Stellar parametrization from Gaia RVS spectra

Among the myriad of data collected by the ESA Gaia satellite, about 150 million spectra will be delivered by the Radial Velocity Spectrometer (RVS) for stars as faint as G_RVS~16. A specific stellar parametrization will be performed for most of these RVS spectra. Some individual chemical abundances will also be estimated for the brightest targets. We describe the different parametrization codes that have been specifically developed or adapted for RVS spectra within the GSP-spec working group of the analysis consortium. The tested codes are based on optimization (FERRE and GAUGUIN), projection (MATISSE) or pattern recognition methods (Artificial Neural Networks). We present and discuss their expected performances in the recovered stellar atmospheric parameters (Teff, log(g), [M/H]) for B- to K- type stars. The performances for the determinations of [alpha/Fe] ratios are also presented for cool stars. For all the considered stellar types, stars brighter than G_RVS~12.5 will be very efficiently parametrized by the GSP-spec pipeline, including solid estimations of [alpha/Fe]. Typical internal errors for FGK metal-rich and metal-intermediate stars are around 40K in Teff , 0.1dex in log(g), 0.04dex in [M/H], and 0.03dex in [alpha/Fe] at G_RVS=10.3. Similar accuracies in Teff and [M/H] are found for A-type stars, while the log(g) derivation is more accurate. For the faintest stars, with G_RVS>13-14, a spectrophotometric Teff input will allow the improvement of the final GSP-spec parametrization. The reported results show that the contribution of the RVS based stellar parameters will be unique in the brighter part of the Gaia survey allowing crucial age estimations, and accurate chemical abundances. This will constitute a unique and precious sample for which many pieces of the Milky Way history puzzle will be available, with unprecedented precision and statistical relevance.

A cloud-integrated web platform for marine monitoring using GIS and remote sensing. Application to oil spill detection through SAR images

Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detection and localization of marine spills using remote sensing, GIS, and cloud computing. Advanced segmentation algorithms are presented in order to isolate dark areas in SAR images, including fuzzy clustering and wavelets. In addition, cloud computing is used for scaling up the algorithms and providing communication between users.

SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey

Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in the sky in the visible magnitude range from 6 to 20, up to approximately 10^9 sources. An international scientific consortium, the Gaia Data Processing and Analysis Consortium (Gaia DPAC), has organized itself in several coordination units, with the aim, among others, of addressing the work of classifying the observed astronomical sources, using both supervised and unsupervised classification algorithms. This work focuses on the analysis of classification outliers by means of unsupervised classification. We present a novel method to combine SOMs trained with independent features that are calculated from spectrophotometry. The method as described here can help to improve the models used for the supervised classification of astronomical sources. Furthermore, it allows for data exploration and knowledge discovery in huge astronomical databases such as the upcoming Gaia mission.

Automated knowledge-based analysis and classification of stellar spectra using fuzzy reasoning

Abstract This paper presents the application of artificial intelligence techniques to optical spectroscopy, a specific field of Astrophysics. We propose the analysis, design and implementation of an intelligent system for the analysis and classification of the low-resolution optical spectra of supergiant, giant and dwarf stars, with luminosity levels I, III and V, respectively. The developed system automatically and objectively collects the most important spectral features, and determines the temperature and luminosity of the stars according to the current standard system. The system development combines signal processing, expert systems and fuzzy logic techniques, and integrates them through the use of a relational database, which allows us to structure the collected astronomical data and to contrast the results of the different classification methods. As an additional research, we have designed and implemented several models of artificial neural networks, including them as an alternative method for the classification of spectra.

HSC: A multi-resolution clustering strategy in Self-Organizing Maps applied to astronomical observations

This work presents a strategy for the classification of astronomical objects based on spectrophotometric data and the use of unsupervised neural networks and statistical classification algorithms. Our strategy constitutes an essential part of the preparation phase of the automatic classification and parameterization algorithms for the data that are to be collected by the Gaia satellite of the European Space Agency (ESA), whose launch is foreseen for the spring of 2012. The proposed algorithm is based on a hierarchical structure of neural networks composed of various tree-structured SOM networks. The classification of possible astronomical objects (stars, galaxies, quasars, multiple objects, etc.) basically consists in the iterative segmentation of the inputs space and the ensuing generation of initial classifications and increase in classification precision by means of a refining process. Apart from providing a classification, our technique also measures the quality and precision of the classifications and segments the objects for which it cannot determine whether or not they belong to a pre-established class of astronomical objects (outliers).

Detection of a multishell planetary nebula around the hot subdwarf O-type star 2MASS J19310888+4324577

Context. The origin of hot subdwarf O-type stars (sdOs) remains unclear since their discovery in 1947. Among others, a post- Asymptotic Giant Branch (post-AGB) origin is possible for a fraction of sdOs. Aims. We are involved in a comprehensive ongoing study to search for and to analyze planetary nebulae (PNe) around sdOs with the aim of establishing the fraction and properties of sdOs with a post-AGB origin. Methods. We use deep Hand (Oiii) images of sdOs to detect nebular emission and intermediate resolution, long-slit optical spec- troscopy of the detected nebulae and their sdO central stars. These data are complemented with other observations (archive images, high-resolution, long-slit spectroscopy) for further ana lysis of the detected nebulae. Results. We report the detection of an extremely faint, complex PN around 2MASS J19310888+4324577 (2M1931+4324), a star classified as sdO in a binary system. The PN shows a bipolar and an elliptical shell, whose major axes are oriented perpendi cular to each other, and high-excitation structures outside the two shells. WISE archive images show faint, extended emission at 12µm and 22µm in the inner nebular regions. The internal nebular kinematics, derived from high resolution, long-slit spectra, is co nsistent with a bipolar and a cylindrical/ellipsoidal shell, in both cases with the main axis mainly perpendicular to the line of sight. The nebular spectrum only exhibits H�, Hand (Oiii)��4959,5007 emission lines, but suggests a very low-excitation ((Oiii)/H�≃1.5), in strong contrast with the absence of low-excitation emission lines. The spectrum of 2M1931+4324 presents narrow, ionized helium absorp- tions that confirm the previous sdO classification and sugges t an effective temperature≥60000 K. The binary nature of 2M1931+4324, its association with a complex PN, and several properties of the system provide strong support for the idea that binary central stars are a crucial ingredient in the formation of complex PNe.

A comparative study of KBS, ANN and statistical clustering techniques for unattended stellar classification

The purpose of this work is to present a comparative analysis of knowledge-based systems, artificial neural networks and statistical clustering algorithms applied to the classification of low resolution stellar spectra. These techniques were used to classify a sample of approximately 258 optical spectra from public catalogues using the standard MK system. At present, we already dispose of a hybrid system that carries out this task, applying the most appropriate classification method to each spectrum with a success rate that is similar to that of human experts.

Parameterization of RVS synthetic stellar spectra for the ESA Gaia mission: Study of the optimal domain for ANN training

One of the upcoming cornerstone missions of the European Space Agency (ESA) is Gaia, a spacecraft that will be launched in 2011 and will carry out a stereoscopic census of our Galaxy and its environment by measuring with unprecedented exactitude the astrometry (distance and movements), the photometric distribution from ultraviolet to the infrared of its components, and, in the case of the brightest objects (mainly stars), the spectrum with intermediate resolution in the region of the infrared CaII triplet, with a spectrograph known as Radial Velocity Spectrometer (RVS). Stars are the basic constituents of our Galaxy, and they can be characterized if we can estimate their principal atmospheric parameters: effective temperature, gravity, metal content (general abundance of elements other than H and He), and their abundance of alpha elements (elements with Z>22, [@a/Fe]), which provide information on the physical environment in which the star was born. This work presents our results for the parameterization of stellar spectra with simulated data (synthetic spectra) in the spectral region of the RVS and with the application of Artificial Intelligence Techniques based on ANNs. Our work has two main purposes: to determine the optimal domain for the ANNs performance, and to develop an adequate noise detection and filtering algorithm.