NASA has funded a project called the Hunt for Observable Signatures of Terrestrial Systems (HOSTS) to survey nearby solar type stars to determine the amount of warm zodiacal dust in their habitable zones. The goal is not only to determine the luminosity distribution function but also to know which individual stars have the least amount of zodiacal dust. It is important to have this information for future missions that directly image exoplanets as this dust is the main source of astrophysical noise for them. The HOSTS project utilizes the Large Binocular Telescope Interferometer (LBTI), which consists of two 8.4-m apertures separated by a 14.4-m baseline on Mt. Graham, Arizona. The LBTI operates in a nulling mode in the mid-infrared spectral window (8-13 mu m), in which light from the two telescopes is coherently combined with a 180 degree phase shift between them, producing a dark fringe at the location of the target star. In doing so the starlight is greatly reduced, increasing the contrast, analogous to a coronagraph operating at shorter wavelengths. The LBTI is a unique instrument, having only three warm reflections before the starlight reaches cold mirrors, giving it the best photometric sensitivity of any interferometer operating in the mid-infrared. It also has a superb Adaptive Optics (AO) system giving it Strehl ratios greater than 98% at 10 mu m. In 2014 into early 2015 LBTI was undergoing commissioning. The HOSTS project team passed its Operational Readiness Review (ORR) in April 2015. The team recently published papers on the target sample, modeling of the nulled disk images, and initial results such as the detection of warm dust around eta Corvi. Recently a paper was published on the data pipeline and on-sky performance. An additional paper is in preparation on beta Leo. We will discuss the scientific and programmatic context for the LBTI project, and we will report recent progress, new results, and plans for the science verification phase that started in February 2016, and for the survey.

During the last decade, the transit method has evolved to one of the most promising techniques in the search for extrasolar planets and the quest to find other earth-like worlds. In theory, the transit method is straight forward being based on the detection of an apparent dimming of the host star’s light due to an orbiting planet traversing in front of the observer. However, in practice, the detection of such light curve dips and their confident ascription to a planetary transit is heavily burdened by the presence of different sources of noise, the most prominent of which is probably the so called intrinsic stellar variability. Filtering out potential transit signals from background noise requires a well adjusted high-pass filter. In order to optimize such a filter, i.e. to achieve best separation between signal and noise, one typically requires access to benchmark datasets that exhibit the same light curve with and without obstructing noise. Several methods for simulating stellar variability have been proposed for the construction of such benchmark datasets. However, while such methods have been widely used in testing transit method detection algorithms in the past, it is not very well known how such simulations compare to real recorded light curves - a fact that might be contributed to the lack of large databases of stellar light curves for comparisons at that time. With the increasing amount of light curve data now available due to missions such as Kepler, I have here undertaken such a comparison of synthetic and real light curves for one particular method that simulates stellar variability based on scaled power spectra of the Sun’s flux variations. Conducting the respective comparison also in terms of estimated power spectra of real and simulated light curves, I have revealed that the two datasets exhibit substantial differences in average power, with the synthetic power spectra having generally a lower power and also lacking certain distinct power peaks present in the real light curves. The results of this study suggest that scaled power spectra of solar variability alone might be insufficient for light curve simulations and that more work will be required to understand the origin and relevance of the observed power peaks in order to improve on such light curve models.

The detection of exoplanets has rapidly evolved to one of the most important frontiers of astronomical and astrophysical research. The recent decades have seen the development of various techniques for detecting exoplanets. Of these approaches the transit method has received particular interest and has lead to the largest number of discoveries to date. The Kepler K2 mission is an ongoing observational survey, which has generated light curves for thousands of stars, a large fraction of which have yet to be fully explored. To discover and characterize the transiting planets hosted by the respective stars, extensive transit screens are required. However, implementing a pipeline for transit analyses is not straight forward, considering the light curve properties of different survey, the rapid changes brought by technological advancements, and the apparent lack of a golden standard with respect to the applied methodology. The project has reviewed several aspects of exoplanet detection via the transit method. Particular focus was placed on the identification of a suitable workflow covering the relevant steps to move from raw light curve files to a final prediction and characterization of transiting planetary candidates. Adhering to the identified strategy, the major part of the project then dealt with the implementation of a pipeline that integrates and executes all the different steps in a streamlined fashion. Of note, primary focus was placed on the actual selection and implementation of methods into an operational pipeline, but due to the given time constraints extensive optimizations of each individual processing step was outside the scope of this project. Nevertheless, the pipeline was employed to predict transit candidates for K2 campaigns C7, C8, C10, C11, and C12. A comparsion of the most conservative predictions from campaigns C7 and C10 with previously reported exoplanet candidates demonstrated that the pipeline was highly capable of discovering reliable transit candidates. Since campaigns C11 and C12 have not yet been fully explored, the respective candidates predicted for those campaigns in the current project might thus harbour novel planetary transit candidates that would be suitable for follow-up confirmation runs. In summary, the current project has produced a pipeline for performing transiting exoplanet searches in K2 data, which integrates the steps from raw light curve processing to transit candidate selection and characterization. The pipeline has been demonstrated to predict credible transit candidates, but future work will have to focus on additional optimizations of individual method parameters and on the analysis of transit detection efficiencies.

Measuring masses of long-period planets around F, G, and K stars is necessary to characterize exoplanets and assess their habitability. Imaging stellar astrometry offers a unique opportunity to solve radial velocity system inclination ambiguity and determine exoplanet masses. The main limiting factor in sparse-field astrometry, besides photon noise, is the non-systematic dynamic distortions that arise from perturbations in the optical train. Even space optics suffer from dynamic distortions in the optical system at the sub-mu as level. To overcome this limitation we propose a diffractive pupil that uses an array of dots on the primary mirror creating polychromatic diffraction spikes in the focal plane, which are used to calibrate the distortions in the optical system. By combining this technology with a high-performance coronagraph, measurements of planetary systems orbits and masses can be obtained faster and more accurately than by applying traditional techniques separately. In this paper, we present the results of the combined astrometry and and high-contrast imaging experiments performed at NASA Ames Research Center as part of a Technology Development for Exoplanet Missions program. We demonstrated 2.38x10(-5) lambda/D astrometric accuracy per axis and 1.72x10(-7) raw contrast from 1.6 to 4.5 lambda/D. In addition, using a simple average subtraction post-processing we demonstrated no contamination of the coronagraph field down to 4.79x10(-9) raw contrast.

Signal acquisition, signal reconstruction and analysis of spectrum of the signal are the three most important steps in signal processing and they are found in almost all of the modern day hardware. In most of the signal processing hardware, the signal of interest is sampled at uniform intervals satisfying some conditions like Nyquist rate. However, in some cases the privilege of having uniformly sampled data is lost due to some constraints on the hardware resources. In this thesis an important problem of signal reconstruction and spectral analysis from nonuniformly sampled data is addressed and a variety of methods are presented. The proposed methods are tested via numerical experiments on both artificial and real-life data sets. The thesis starts with a brief review of methods available in the literature for signal reconstruction and spectral analysis from non uniformly sampled data. The methods discussed in the thesis are classified into two broad categories - dense and sparse methods, the classification is based on the kind of spectra for which they are applicable. Under dense spectral methods the main contribution of the thesis is a non-parametric approach named LIMES, which recovers the smooth spectrum from non uniformly sampled data. Apart from recovering the spectrum, LIMES also gives an estimate of the covariance matrix. Under sparse methods the two main contributions are methods named SPICE and LIKES - both of them are user parameter free sparse estimation methods applicable for line spectral estimation. The other important contributions are extensions of SPICE and LIKES to multivariate time series and array processing models, and a solution to the grid selection problem in sparse estimation of spectral-line parameters. The third and final part of the thesis contains applications of the methods discussed in the thesis to the problem of radial velocity data analysis for exoplanet detection. Apart from the exoplanet application, an application based on Sudoku, which is related to sparse parameter estimation, is also discussed.

The detection of low-mass extra-solar planets through radial-velocity searches is currently limited by the intrinsic magnetic activity of the host stars. The correlated noise that arises from their natural radial-velocity variability can easily mimic or conceal the orbital signals of super-Earth and Earth-mass extra-solar planets. I developed an intuitive and robust data analysis framework in which the activity-induced variations are modelled with a Gaussian process that has the frequency structure of the photometric variations of the star, thus allowing me to determine precise and reliable planetary masses. I applied this technique to three recently discovered planetary systems: CoRoT-7, Kepler-78 and Kepler-10. I determined the masses of the transiting super-Earth CoRoT-7b and the small Neptune CoRoT-7c to be 4.73 ± 0.95 M⊕ and 13.56 ± 1.08 M⊕, respectively. The density of CoRoT-7b is 6.61 ± 1.72 g.cm⁻³, which is compatible with a rocky composition. I carried out Bayesian model selection to assess the nature of a previously identified signal at 9 days, and found that it is best interpreted as stellar activity. Despite the high levels of activity of its host star, I determined the mass of the Earth-sized planet Kepler-78b to be 1.76 ± 0.18 M⊕. With a density of 6.2(+1.8:-1.4) g.cm⁻³, it is also a rocky planet. I found the masses of Kepler-10b and Kepler-10c to be 3.31 ± 0.32 M⊕ and 16.25 ± 3.66 M⊕, respectively. Their densities, of 6.4(+1.1:-0.7) g.cm⁻³ and 8.1 ± 1.8 g.cm⁻³, imply that they are both of rocky composition – even the 2 Earth-radius planet Kepler-10c! In parallel, I deepened our understanding of the physical origin of stellar radial-velocity variability through the study of the Sun, which is the only star whose surface can be imaged at high resolution. I found that the full-disc magnetic flux is an excellent proxy for activity-induced radial-velocity variations; this result may become key to breaking the activity barrier in coming years. I also found that in the case of CoRoT-7, the suppression of convective blueshift leads to radial-velocity variations with an rms of 1.82 m.s⁻¹, while the modulation induced by the presence of dark spots on the rotating stellar disc has an rms of 0.46 m.s⁻¹. For the Sun, I found these contributions to be 2.22 m.s⁻¹ and 0.14 m.s⁻¹, respectively. These results suggest that for slowly rotating stars, the suppression of convective blueshift is the dominant contributor to the activity-modulated radial-velocity signal, rather than the rotational Doppler shift of the flux blocked by starspots.