A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

[Abstract] This paper analyzes and compares the sensitivity and suitability of several artificial intelligence techniques applied to the Morgan–Keenan (MK) system for the classification of stars. The MK system is based on a sequence of spectral prototypes that allows classifying stars according to their effective temperature and luminosity through the study of their optical stellar spectra. Here, we include the method description and the results achieved by the different intelligent models developed thus far in our ongoing stellar classification project: fuzzy knowledge-based systems, backpropagation, radial basis function (RBF) and Kohonen artificial neural networks. Since one of today’s major challenges in this area of astrophysics is the exploitation of large terrestrial and space databases, we propose a final hybrid system that integrates the best intelligent techniques, automatically collects the most important spectral features, and determines the spectral type and luminosity level of the stars according to the MK standard system. This hybrid approach truly emulates the behavior of human experts in this area, resulting in higher success rates than any of the individual implemented techniques. In the final classification system, the most suitable methods are selected for each individual spectrum, which implies a remarkable contribution to the automatic classification process.This work was supported by Ministry of Science, Innovation and Universities (FEDER RTI2018-095076-B-C22) and Xunta de Galicia (ED431B 2018/42)Xunta de Galicia; ED431B 2018/4

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.Fil: Abdurro'Uf, null. Academia Sinica, Institute Of Astronomy And Astrophysics; ChinaFil: Accetta, Katherine. Prynceton University, ; Estados UnidosFil: Aerts, Conny. Katholikie Universiteit Leuven; BélgicaFil: Silva Aguirre, Víctor. Aarhus University. Department of Bioscience; DinamarcaFil: Ahumada, Romina. Universidad Católica del Norte; ChileFil: Ajgaonkar, Nikhil. University of Kentucky; Estados UnidosFil: Filiz Ak, N.. Erciyes University; TurquíaFil: Alam, Shadab. University of Edinburgh; Reino UnidoFil: Allende Prieto, Carlos. Instituto de Astrofísica de Canarias, Tenerife; EspañaFil: Almeida, Andrés. University of Virginia; Estados UnidosFil: Anders, Friedrich. Leibniz-Institut fur Astrophysik Potsdam; AlemaniaFil: Anderson, Scott F.. University of Washington; Estados UnidosFil: Andrews, Brett H.. University of Pittsburgh; Estados UnidosFil: Anguiano, Borja. University of Virginia; Estados UnidosFil: Aquino Ortiz, Erik. Universidad Nacional Autónoma de México; MéxicoFil: Aragón Salamanca, Alfonso. University of Nottingham; Estados UnidosFil: Argudo Fernández, Maria. Pontificia Universidad Católica de Valparaíso; ChileFil: Ata, Metin. University of Tokyo; JapónFil: Aubert, Marie. Aix Marseille Université; FranciaFil: Avila Reese, Vladimir. Universidad Nacional Autónoma de México; MéxicoFil: Badenes, Carles. University of Pittsburgh; Estados UnidosFil: Barbá, Rodolfo. Universidad de La Serena; ChileFil: Barger, Kat. Texas Christian University; Estados UnidosFil: Barrera Ballesteros, Jorge K,. Universidad Nacional Autónoma de México; MéxicoFil: Beaton, Rachael L.. Princeton University; Estados UnidosFil: Beers, Timothy C.. University of Notre Dame; Estados UnidosFil: Belfiore, Francesco. Istituto Nazionale di Astrofisica; ItaliaFil: Bender, Chad F.. University of Arizona; Estados UnidosFil: Bernardi, Mariangela. State University of Pennsylvania; Estados UnidosFil: Bershady, Matthew A.. University of Wisconsin; Estados UnidosFil: Monachesi, Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad de La Serena; ChileFil: Padilla, Nelson David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentin

The seventeenth Data Release of the Sloan Digital Sky Surveys : complete release of MaNGA, MaStar, and APOGEE-2 data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

New contributions to algorithms and tools for the analysis of photometric and spectroscopic time-series in exoplanet searches

[eng] The current trend in exoplanet research focuses on the detection and characterisation of Earth-sized planets, and the study of their potential subtle and tenuous atmospheres. The aim of this thesis is the development of tools and simulation codes for the detection and characterisation of exoplanets by means of the indirect methods of radial velocities and transits. The structure of the thesis is two-fold. Firstly, we present a multidimensional extension to the well-known period search GLS code, which we dub MGLS (Multidimensional Generalized Lomb-Scargle periodogram). The analysis of a time-series periodogram of radial velocity data is the usual starting point to seek for periodic signals which then can be associated with the reflex Keplerian motion of a star caused by orbiting exoplanets. In the case of multiplanetary systems such analysis is usually carried out in an iterative fashion, known as prewhitening. This approach can diminish the significance and distort the parameters of periodic signals, and we aim to solve those limitations by introducing a multidimensional approach. Additionally, a robust criterion to determine the number of signals (dimensionality) in a time-series is presented. The new approach is more flexible and enhances the significance of multisignal detections and their multiplicity. It is further better capable to pinpoint the fit parameters and is able to compare models of different dimensionality. The MGLS code has been tested with real multiplanetary systems, showing its excellent performance in detectability. The code is publicly available to the community. The second part addresses the effects of rotationally-induced stellar activity on the photometric and spectroscopic observables. The properties, distribution, and evolution of inhomogeneities on the surface of active stars, such as dark spots and bright faculae, significantly influence the determination of the parameters of an orbiting exoplanet. The chromatic effect they have on transmission spectroscopy, for example, could affect the analysis of data from future space missions such as James Webb Space Telescope (JWST) and Ariel. To quantify and mitigate the effects of those surface phenomena, we developed a fast modelling approach to derive the surface distribution and properties of active regions by modelling simultaneous multi-wavelength time-series observables. We present an upgraded version of the StarSim code, now featuring the capability to solve the inverse problem and derive the properties of the stars and their active regions by modelling time-series data. The multiband photometric inverse problem is both analytically and numerically discussed, as well as a broad analysis of the degeneracies found in the inversion process. As a test case, we analyse a BVRI multiband ground photometry dataset of the exoplanet host star WASP-52. From the results, we further simulated the chromatic contribution of surface phenomena on the observables of its transiting planet. We demonstrate that by using contemporaneous ground-based multiband photometry of an active star, it is possible to reconstruct the parameters and distribution of active regions over time, thus making it feasible to quantify the chromatic effects on the planetary radii measured with transit spectroscopy and mitigate them by about an order of magnitude. The obtained results show it is possible to accurately characterise the heterogeneous stellar surface up to a precision of a few parts in 10^5 and validate the scientific case of space missions like Ariel, designed for exoplanetary transmission spectroscopy.[cat] L'interès actual en la recerca en exoplanetes rau en la detecció de planetes cada cop més petits on els senyals són estadísticament poc significatius, particularment en l'estudi de les seves atmosferes. La tesi té com a objectiu el disseny i desenvolupament d'eines i codis sofisticats per a la detecció i caracterització de les propietats d'exoplanetes mitjançant l'ús de les tècniques de velocitat radial i trànsits, i es compon de dues parts diferenciades: la primera, tracta la generalització d'una eina de detecció de periodicitats molt popular en aquest camp (GLS). S'ha desenvolupat una versió multidimensional, que anomenem MGLS (Multidimensional Generalized Lomb-Scargle periodogram), que permet l'ajust simultani d'un nombre arbitrari de senyals, de manera que millora notablement la detectabilitat de senyals compostos, i evita els problemes derivats del filtratge quan es fa servir el procediment seqüencial, com falsos positius/negatius. Addicionalment es presenta un procediment robust per a la determinació del nombre de senyals (dimensionalitat). La segona part, tracta els efectes de l'activitat estel·lar sobre les mesures de velocitat radial i fotometria. L'activitat magnètica superficial en forma de taques i fàcules constitueix superfícies heterogènies, que amb la rotació de l'estrella, produeixen variacions d'intensitat i cromàtiques en els observables. S'ha desenvolupat un codi ràpid per a la modelització física de l'activitat induïda per rotació de taques, prenent com a base una versió de codi preexistent. En la versió StarSim 2, permet dur a terme el problema invers per determinar l'estat de la superfície més probable donades unes observacions fotomètriques. També es desenvolupa una formulació analítica per al problema invers multibanda i s'analitzen detalladament les degeneracions existents en el problema. L'esquema d'inversió i el codi s'aplica a un conjunt de dades multifiltre (BVRI) de l'estrella WASP-52, i es simulen els efectes cromàtics del model d'activitat ajustat sobre els seus trànsits, com procedir per corregir-los per assolir una precisió d'unes poques parts en cent mil, i per tant validar el cas científic que sustenta la missió Ariel per a l'anàlisi d'atmosferes exoplanetàries per espectroscòpia de transmissió

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

Abstract This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.</jats:p

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys

The Lithium Plateau in Super Metal-Rich Stars

I present a study of the lithium abundances for a sample of super metal-rich (SMR, [Fe/H] > + 0.20) main sequence dwarfs in the solar neighborhood. The SMR stars were selected to have surface temperatures in the region of the lithium plateau, a narrow region in temperature space where stars are predicted by models to preserve their initial surface Li abundances while on the main sequence. Despite these predictions, observations of lower-metallicity stars indicate that significant depletion occurs during the first few Gyr of stellar evolution. SMR stars, which represent the extreme end of Galactic chemical evolution, present an opportunity to constrain proposed mechanisms to explain this depletion. 100 SMR candidates were selected from existing surveys and followed up with spectroscopic observations using the Hydra spectrograph on the WIYN 3.5m telescope at Kitt Peak National Observatory, as well as photometric observations using the 0.9m WIYN and 40” telescopes at Kitt Peak and Mount Laguna Observatory, respectively. Using the results from ANNA, a new tool that uses a neural network to parameterize stellar spectra, as well as a more traditional equivalent width based analysis, 44 single stars with [Fe/H] > + 0.20 were identified and Li was measured for each star. Consistent with previous studies, the SMR stars can be divided into a sample of stars with measurable Li and a sample with upper limits only. Examining the low-Li stars’ evolutionary states reveals that they are consistent with being evolved Li dip stars and therefore depleted their surface Li while on the main sequence before evolving to the cooler temperatures of the Li plateau. Considering only the high-Li sample, the stars are all consistent with having ages in the range 3 – 4.5 Gyr, indicating that they have already significantly depleted their Li. We find no young (age + 0.20) main sequence dwarfs in the solar neighborhood. The SMR stars were selected to have surface temperatures in the region of the lithium plateau, a narrow region in temperature space where stars are predicted by models to preserve their initial surface Li abundances while on the main sequence. Despite these predictions, observations of lower-metallicity stars indicate that significant depletion occurs during the first few Gyr of stellar evolution. SMR stars, which represent the extreme end of Galactic chemical evolution, present an opportunity to constrain proposed mechanisms to explain this depletion. 100 SMR candidates were selected from existing surveys and followed up with spectroscopic observations using the Hydra spectrograph on the WIYN 3.5m telescope at Kitt Peak National Observatory, as well as photometric observations using the 0.9m WIYN and 40” telescopes at Kitt Peak and Mount Laguna Observatory, respectively. Using the results from ANNA, a new tool that uses a neural network to parameterize stellar spectra, as well as a more traditional equivalent width based analysis, 44 single stars with [Fe/H] > + 0.20 were identified and Li was measured for each star. Consistent with previous studies, the SMR stars can be divided into a sample of stars with measurable Li and a sample with upper limits only. Examining the low-Li stars’ evolutionary states reveals that they are consistent with being evolved Li dip stars and therefore depleted their surface Li while on the main sequence before evolving to the cooler temperatures of the Li plateau. Considering only the high-Li sample, the stars are all consistent with having ages in the range 3 – 4.5 Gyr, indicating that they have already significantly depleted their Li. We find no young (age + 0.20 were identified and Li was measured for each star. Consistent with previous studies, the SMR stars can be divided into a sample of stars with measurable Li and a sample with upper limits only. Examining the low-Li stars’ evolutionary states reveals that they are consistent with being evolved Li dip stars and therefore depleted their surface Li while on the main sequence before evolving to the cooler temperatures of the Li plateau. Considering only the high-Li sample, the stars are all consistent with having ages in the range 3 – 4.5 Gyr, indicating that they have already significantly depleted their Li. We find no young (age < 1 Gyr) SMR stars in the sample, which may explain an observed turnover in the positive correlation between [Fe/H] and initial Li abundance at super-solar metallicities – the only SMR stars in the solar neighborhood are already too old to measure initial Li as they have depleted down to the 2-3 Gyr plateau value. The Li plateau for the SMR sample was measured to be A(Li) = 2.55 dex, which agrees with observations of the similarly-aged super metal-rich cluster NGC 6253 as well as more metal-poor clusters, confirming that the Li plateau abundance in stars older than 2-3 Gyr is apparently insensitive to stellar metallicity. Examining the kinematics and available elemental abundances of the SMR stars, they are shown to be indistinguishable from lower-metallicity thin disk stars aside from their high [Fe/H], consistent with an origin in the inner thin disk

Extracción de conocimiento en bases de datos astronómicas mediante redes de neuronas artificiales : aplicaciones en la misión Gaia

[Resumen] En la llamada era de las TIC, las capacidades de los sistemas de adquisición de datos han aumentado enormemente, de forma que resulta complicado almacenar toda la información que producen, así como su análisis posterior. Esta explosión de datos ha aparecido recientemente en el campo de la Astronomía, donde cada vez se observan un número mayor de objetos, con mayor periodicidad. Un ejemplo de esto es la próxima misión Gaia, que observará múltiples propiedades de hasta mil millones de estrellas, cuya información tendrá un volumen del orden del petabyte. Por lo tanto, para analizar tal cantidad de datos, es necesario desarrollar nuevos métodos de análisis que permitan extraer todo el conocimiento presente en los mismos. Esta tesis está dedicada al desarrollo de métodos de análisis de datos, los cuales se integran en la cadena de procesado de Gaia, con el objetivo de extraer conocimiento de los datos recogidos por la misión. Con el objetivo de analizar los datos de la misión Gaia, se ha organizado un consorcio a nivel europeo, llamado Data Processing and Analysis Consortium (DPAC), compuesto por cientos de científicos e ingenieros. DPAC se divide en ocho unidades de coordinación (CUs), estando esta tesis dedicada al desarrollo de algoritmos en la CU8, que se encarga de la estimación de parámetros astrofísicos (APs) y la clasificación de las fuentes. Se desarrollan métodos basados en redes de neuronas artificiales (ANNs) para realizar las tareas relacionadas con dos paquetes de trabajo diferentes en la CU8: El paquete GSP-Spec (GWP-823) y el paquete OA (GWP-836). El paquete GSP-Spec se encarga de la estimación de APs de estrellas mediante el espectro del instrumento Radial Velocity Spectrograph (RVS). Aquí, se presentará el desarrollo de uno de los módulos de GSP-Spec, el cual se basa en la aplicación de ANNs de tipo feed-forward. Se presenta una metodología, basada en algoritmos genéticos de optimización, para la obtención de un conjunto óptimo de parámetros de configuración para la ANN en cada caso, en función de la relación señal a ruido (SNR) en el espectro RVS y del tipo de estrella a parametrizar. Además, con el objetivo de mejorar las estimaciones de APs, se estudian técnicas de procesado wavelet, aplicadas sobre el espectro RVS. A pesar de la efectividad que las ANNs muestran a la hora de estimar APs, en principio éstas carecen de la capacidad de proporcionar un valor de incertidumbre sobre dichas estimaciones, con lo cual resulta imposible conocer la fiabilidad de las mismas. Debido a ello, se presenta una arquitectura novedosa para la ANN, en la cual se invierten las entradas y salidas de la misma, de forma que la ANN estima el espectro RVS a partir de los APs. Dicha arquitectura de denomina red neuronal artificial generativa (GANN) y se aplica a la estimación de APs de un conjunto de espectros RVS simulados para la misión Gaia, donde se muestra más efectiva que el modelo de ANN convencional, en el caso de estrellas débiles, con un bajo SNR. Finalmente, la red GANN puede aplicarse para la obtención de la probabilidad a posteriori de cada uno de los APs dado el espectro RVS, lo cual permitirá un análisis más completo de los mismos. Dada la naturaleza de la misión Gaia, la cual es la primera misión astronómica que observará, de forma no sesgada, toda la bóveda celeste hasta magnitud 20, se espera una gran cantidad de objetos atípicos. El paquete OA en la CU8 se encarga del procesado de dicho tipo de objetos, los cuales se definen como aquellos que no han podido ser clasificados con fiabilidad por los paquetes de clasificación existentes en la cadena de procesamiento. Los métodos de OA se basan en el aprendizaje no supervisado del conjunto de observaciones atípicas. Dicho aprendizaje tiene dos partes: agrupamiento y reducción de dimensionalidad. Se seleccionan los mapas auto-organizativos (SOM) como algoritmo base para realizar dicho aprendizaje, demostrándose su efectividad cuando se aplica, con una configuración óptima, a las simulaciones de Gaia. Además, el algoritmo es aplicado a observaciones atípicas reales, provenientes del catálogo SDSS. Dado que es necesaria una identificación posterior de los grupos obtenidos por la red SOM, se aplican dos métodos de identificación diferentes. El primero está basado en la similitud entre los prototipos de la red y el conjunto de simulaciones de Gaia, mientras que el otro esa basado en la recuperación de clasificaciones almacenadas en el catálogo Simbad, mediante el cruce de coordenadas celestes. Gracias a la visualización de la red SOM, y a ambos métodos de identificación, es posible distinguir entre observaciones válidas y artefactos observacionales. Además, el método posibilita la selección de objetos de interés para observaciones posteriores, con el objetivo de determinar la naturaleza de los mismos.[Abstract] In the so-called IT era, the capabilities of data acquisition systems have increased to such an extent that it has become difficult to store all the information they produce, and analyse it. This explosion of data has recently appeared in the field of Astronomy, where an increasing number of objects are being observed on a regular basis. An example of this is the upcoming Gaia mission, which will pick up multiple properties of a billion stars, whose information will have a volume of approximately a petabyte. The analysis of a similar amount of information inevitably requires the development of new data analysis methods to extract all the knowledge it contains. This thesis is devoted to the development of data analysis methods to be integrated in the Gaia pipeline, such that knowledge can be extracted from the data collected by the mission. In order to analyze the data from the Gaia mission, the European Space Agency organized the Data Processing and Analysis Consortium (DPAC) which is composed of hundreds of scientists and engineers. DPAC is divided into eight Coordination Units (CUs). This thesis is dedicated to algorithm development in CU8, which is responsible for source classification and astrophysical parameters (AP) estimation. Methods based on Artificial Neural Networks (ANNs) are developed to perform the tasks related to two different work packages in CU8: the GSP-Spec package (GWP-823), and the OA package (GWP-836). The GSP-Spec package is responsible for estimating stellar APs by means of the Radial Velocity Spectrograph (RVS) spectrum. This work presents the development of one of the GSP-Spec modules, which is based on the application of feed-forward ANNs. A methodology is described, based on the optimization of genetic algorithms and aimed at obtaining an optimal set of configuration parameters for the ANN in each case, depending on the signal to noise ratio (SNR) in the RVS spectrum and on the type of star to parameterize. Furthermore, in order to improve the AP estimates, wavelet signal processing techniques, applied to the RVS spectrum, are studied. Despite the effectiveness shown by ANNs in estimating APs, in principle they lack the ability to provide an uncertainty value on these estimates, making it impossible to determine their reliability. Because of this, a new architecture for the ANN is presented in which the inputs and outputs are reversed, so that the ANN estimates the RVS spectrum from the APs. Such an architecture is called Generative ANN (GANN) and is applied to the AP estimation of a set of simulated RVS spectra for the Gaia mission, where it is more effective than the conventional ANN model, in the case of faint stars with low SNR. Finally, the GANN can be applied for obtaining the posterior probability of each of the APs according to the RVS spectrum, allowing for their more complete analysis. Given the nature of the Gaia mission, which is the first astronomical mission that will observe, in an unbiased way, the entire sky up to magnitude 20, a large number of outliers are expected. The OA package in CU8 handles the processing of this type of objects, which are defined as those that could not be reliably classified by the methods in the upstream classification packages. OA methods are based on the unsupervised learning of all outliers. Such learning has two parts: clustering and dimensionality reduction. The Self-Organizing Map (SOM) algorithm is selected as a basis for this learning. Its effectiveness is demonstrated when it is applied, with an optimal configuration, to the Gaia simulations. Furthermore, the algorithm is applied to real outliers from the SDSS catalog. Since a subsequent identification of the clusters obtained by the SOM is necessary, two different methods of identification are applied. The first method is based on the similarity between the SOM prototypes and the Gaia simulations, and the second method is based on the recovery of stored classifications in the SIMBAD catalog by cross-matching celestial coordinates. Thanks to the visualization of the SOM planes, and to both methods of identification, it is possible to distinguish between valid observations and observational artifacts. Furthermore, the method allows for the selection of objects of interest for follow-up observations, in order to determine their nature