We image 104 newly identified low-mass (mostly M-dwarf) pre-main sequence (PMS) members of nearby young moving groups (YMGs) with Magellan Adaptive Optics (MagAO) and identify 27 stellar binaries with instantaneous projected separation as small as 40 mas. Fifteen were previously unknown. The total number of multiple systems in this sample including spectroscopic and visual binaries from the literature is 36, giving a raw stellar multiplicity rate of at least 35(-4)(+5)% for this population. In the separation range of roughly 1-300 au in which infrared AO imaging is most sensitive, the raw multiplicity rate is at least 24(-4)(+5)% for binaries resolved by the MagAO infrared camera (Clio). The M-star subsample of 87 stars yields a raw multiplicity of at least 30(-4)(+5)% over all separations, 21(-4)(+5)% for secondary companions resolved by Clio from 1 to 300 au (23(-4)(+5)% for all known binaries in this separation range). A combined analysis with binaries discovered by the Search for Associations Containing Young stars shows that stellar multiplicity fraction as a function of mass over the range of 0.2 to 1.2M(circle dot) appears to be linearly flat, in contrast to the field, where multiplicity increases with mass. After bias corrections are applied, the multiplicity of low-mass YMG members (0.2-0.6M(circle dot)) is in excess of the field. The overall multiplicity fraction is also consistent with being constant in age and across YMGs, which suggests that multiplicity rates for this mass range are largely set by 10 Myr without appreciable evolution thereafter.

Comprehensive and robust analysis of galaxies found throughout cosmic time provides the means to probe the underlying characteristics of our Universe. Coupling observations and theory, spectral energy distribution (SED) fitting provides a method to derive the intrinsic properties of distant galaxies which then aid in defining galaxy populations and constraining current galaxy formation and evolution scenarios. One such population are the sub-millimeter galaxies (SMGs) whose high infrared luminosities -- typically associated with dust-obscured star formation -- and redshift distribution places them as likely key components in galaxy evolution. To fully analyze these systems, however, requires a near complete sampling of the full SED, detailed models that encapsulate the variety of physical processes and sophisticated methods for comparing the data and models. In this dissertation, we present the general propose, Monte Carlo Markov Chain (MCMC) based SED fitting routine SED Analysis Through Markov Chains (SATMC) and the insight we have gained in modeling a sample of AzTEC 1.1mm-detected SMGs. The MCMC engine and Bayesian formalism used in the construction of SATMC offers a unique view at the constraints on model parameter space that are often grossly simplified in traditional SED fitting methods. We first present the motivation behind SATMC and its MCMC algorithm. We also highlight a series of test cases that verify not only its reliability but its versatility to various astrophysical applications, including the field of photometric redshift estimation. We then present the AzTEC SMG sample and preliminary results obtained through counterpart identification, X-ray spectral modeling and SED fitting with SATMC. Finally, we present the latest work in detailed SED analysis of SMGs and how these results influence our understanding of the SMG population.

No description available.

The nature of the thermal structure of hot Jupiter atmospheres is one of the key questions raised by the characterization of transiting exoplanets over the past decade. There have been claims that many hot Jupiters exhibit atmospheric thermal inversions. However, these claims have been based on broadband photometry rather than the unambiguous identification of emission features with spectroscopy, and the chemical species that could cause the thermal inversions by absorbing stellar irradiation at high altitudes have not been identified despite extensive theoretical and observational effort. Here we present high-precision Hubble Space Telescope WFC3 observations of the dayside thermal emission spectrum of the hot Jupiter HD 209458b, which was the first exoplanet suggested to have a thermal inversion. In contrast to previous results for this planet, our observations detect water in absorption at 6.2 sigma confidence. When combined with Spitzer photometry, the data are indicative of a monotonically decreasing temperature with pressure over the range of 1-0.001 bars at 7.7 sigma confidence. We test the robustness of our results by exploring a variety of model assumptions, including the temperature profile parameterization, presence of a cloud, and choice of Spitzer data reduction. We also introduce a new analysis method to determine the elemental abundances from the spectrally retrieved mixing ratios with thermochemical self-consistency and find plausible abundances consistent with solar metallicity (0.06-10 x solar) and carbon-to oxygen ratios less than unity. This work suggests that high-precision spectrophotometric results are required to robustly infer thermal structures and compositions of extrasolar planet atmospheres and to perform comparative exoplanetology.

Context. Radial velocity and transit methods are effective for the study of short orbital period exoplanets but they hardly probe objects at large separations for which direct imaging can be used. Aims. We carried out the international deep planet survey of 292 young nearby stars to search for giant exoplanets and determine their frequency. Methods. We developed a pipeline for a uniform processing of all the data that we have recorded with NIRC2/Keck II, NIRI/Gemini North, NICI/Gemini South, and NACO/VLT for 14 yr. The pipeline first applies cosmetic corrections and then reduces the speckle intensity to enhance the contrast in the images. Results. The main result of the international deep planet survey is the discovery of the HR8799 exoplanets. We also detected 59 visual multiple systems including 16 new binary stars and 2 new triple stellar systems, as well as 2279 point-like sources. We used Monte Carlo simulations and the Bayesian theorem to determine that 1.05(-0.70)(+2.80)% of stars harbor at least one giant planet between 0.5 and 14 MJ and between 20 and 300AU. This result is obtained assuming uniform distributions of planet masses and semi-major axes. If we consider power law distributions as measured for close-in planets instead, the derived frequency is 2.30(-1.55)(+5.95)%, recalling the strong impact of assumptions on Monte Carlo output distributions. We also find no evidence that the derived frequency depends on the mass of the hosting star, whereas it does for close-in planets. Conclusions. The international deep planet survey provides a database of confirmed background sources that may be useful for other exoplanet direct imaging surveys. It also puts new constraints on the number of stars with at least one giant planet reducing by a factor of two the frequencies derived by almost all previous works.

Diss. (sammanfattning) Umeå : Sveriges lantbruksuniv., 2006. / Härtill 4 uppsatser.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiv, 155 p.; also includes graphics (some col.). Includes bibliographical references (p. 147-155). Available online via OhioLINK's ETD Center

This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7).

Il semble maintenant établi que la majorité des étoiles se forment dans des amas (Carpenter 2000; Porras et al. 2003; Lada & Lada 2003). Comprendre l'origine et l'évolution des populations stellaires est donc l'un des plus grands défis de l'astrophysique moderne. Malheureusement, moins d'un dixième de ces amas restent gravitationellement liés au delà de quelques centaines de millions d'années (Lada & Lada 2003). L’étude des amas stellaires doit donc se faire avant leur dissolution dans la galaxie.Le projet Dynamical Analysis of Nearby Clusters (DANCe, Bouy et al. 2013), dont le travail fait partie, fournit le cadre scientifique pour l'analyse des amas proches et jeunes (NYC) dans le voisinage solaire. Les observations de l'amas ouvert des Pléiades par le projet DANCe offrent une opportunité parfaite pour le développement d'outils statistiques visant à analyser les premières phases de l'évolution des amas.L'outil statistique développé ici est un système intelligent probabiliste qui effectue une inférence bayésienne des paramètres régissant les fonctions de densité de probabilité (PDF) de la population de l'amas (PDFCP). Il a été testé avec les données photométriques et astrométriques des Pléiades du relevé DANCe. Pour éviter la subjectivité de ces choix des priors, le système intelligent les établit en utilisant l'approche hiérarchique bayésienne (BHM). Dans ce cas, les paramètres de ces distributions, qui sont également déduits des données, proviennent d'autres distributions de manière hiérarchique.Dans ce système intelligent BHM, les vraies valeurs du PDFCP sont spécifiées par des relations stochastiques et déterministes représentatives de notre connaissance des paramètres physiques de l'amas. Pour effectuer l'inférence paramétrique, la vraisemblance (compte tenu de ces valeurs réelles), tient en compte des propriétés de l'ensemble de données, en particulier son hétéroscédasticité et des objects avec des valeurs manquantes.Le BHM obtient les PDF postérieures des paramètres dans les PDFCP, en particulier celles des distributions spatiales, de mouvements propres et de luminosité, qui sont les objectifs scientifiques finaux du projet DANCe. Dans le BHM, chaque étoile du catalogue contribue aux PDF des paramètres de l'amas proportionnellement à sa probabilité d'appartenance. Ainsi, les PDFCP sont exempts de biais d'échantillonnage résultant de sélections tronquées au-dessus d'un seuil de probabilité défini plus ou moins arbitrairement.Comme produit additionnel, le BHM fournit également les PDF de la probabilité d'appartenance à l'amas pour chaque étoile du catalogue d'entrée, qui permettent d'identifier les membres probables de l'amas, et les contaminants probables du champ. La méthode a été testée avec succès sur des ensembles de données synthétiques (avec une aire sous la courbe ROC de 0,99), ce qui a permis d'estimer un taux de contamination pour les PDFCP de seulement 5,8 %.Ces nouvelles méthodes permettent d'obtenir et/ou de confirmer des résultats importants sur les propriétés astrophysiques de l'amas des Pléiades. Tout d'abord, le BHM a découvert 200 nouveaux candidats membres, qui représentent 10% de la population totale de l'amas. Les résultats sont en excellent accord (99,6% des 100 000 objets dans l'ensemble de données) avec les résultats précédents trouvés dans la littérature, ce qui fournit une validation externe importante de la méthode. Enfin, la distribution de masse des systèmes actuelle (PDSMD) est en général en bon accord avec les résultats précédents de Bouy et al. 2015, mais présente l'avantage inestimable d'avoir des incertitudes beaucoup plus robustes que celles des méthodes précédentes.Ainsi, en améliorant la modélisation de l'ensemble de données et en éliminant les restrictions inutiles ou les hypothèses simplificatrices, le nouveau système intelligent, développé et testé dans le présent travail, représente l'état de l'art pour l'analyse statistique des populations de NYC. / The origin and evolution of stellar populations is one of the greatest challenges in modern astrophysics. It is known that the majority of the stars has its origin in stellar clusters (Carpenter 2000; Porras et al. 2003; Lada & Lada 2003). However, only less than one tenth of these clusters remains bounded after the first few hundred million years (Lada & Lada 2003). Ergo, the understanding of the origin and evolution of stars demands meticulous analyses of stellar clusters in these crucial ages.The project Dynamical Analysis of Nearby Clusters (DANCe, Bouy et al. 2013), from which the present work is part of, provides the scientific framework for the analysis of Nearby Young Clusters (NYC) in the solar neighbourhood (< 500 pc). The DANCe carefully designed observations of the well known Pleiades cluster provide the perfect case study for the development and testing of statistical tools aiming at the analysis of the early phases of cluster evolution.The statistical tool developed here is a probabilistic intelligent system that performs Bayesian inference for the parameters governing the probability density functions (PDFs) of the cluster population (PDFCP). It has been benchmarked with the Pleiades photometric and astrometric data of the DANCe survey. As any Bayesian framework, it requires the setting up of priors. To avoid the subjectivity of these, the intelligent system establish them using the Bayesian Hierarchical Model (BHM) approach. In it, the parameters of prior distributions, which are also inferred from the data, are drawn from other distributions in a hierarchical way.In this BHM intelligent system, the true values of the PDFCP are specified by stochastic and deterministic relations representing the state of knowledge of the NYC. To perform the parametric inference, the likelihood of the data, given these true values, accounts for the properties of the data set, especially its heteroscedasticity and missing value objects. By properly accounting for these properties, the intelligent system: i) Increases the size of the data set, with respect to previous studies working exclusively on fully observed objects, and ii) Avoids biases associated to fully observed data sets, and restrictions to low-uncertainty objects (sigma-clipping procedures).The BHM returns the posterior PDFs of the parameters in the PDFCPs, particularly of the spatial, proper motions and luminosity distributions. In the BHM each object in the data set contributes to the PDFs of the parameters proportionally to its likelihood. Thus, the PDFCPs are free of biases resulting from typical high membership probability selections (sampling bias).As a by-product, the BHM also gives the PDFs of the cluster membership probability for each object in the data set. These PDFs together with an optimal probability classification threshold, which is obtained from synthetic data sets, allow the classification of objects into cluster and field populations. This by-product classifier shows excellent results when applied on synthetic data sets (with an area under the ROC curve of 0.99). From the analysis of synthetic data sets, the expected value of the contamination rate for the PDFCPs is 5.8 ± 0.2%.The following are the most important astrophysical results of the BHM applied tothe Pleiades cluster. First, used as a classifier, it finds ∼ 200 new candidate members, representing 10% new discoveries. Nevertheless, it shows outstanding agreement (99.6% of the 105 objects in the data set) with previous results from the literature. Second, the derived present day system mass distribution (PDSMD) is in general agreement with the previous results of Bouy et al. (2015).Thus, by better modelling the data set and eliminating unnecessary restrictions to it, the new intelligent system, developed and tested in the present work, represents the state of the art for the statistical analysis of NYC populations.