Stellar mass functions of galaxies at 4<z<7 from an IRAC-selected sample in COSMOS/UltraVISTA: limits on the abundance of very massive galaxies

We build a Spitzer IRAC complete catalog of objects, obtained by complementing the $K_\mathrm{s}$-band selected UltraVISTA catalog with objects detected in IRAC only. With the aim of identifying massive (i.e., $\log(M_*/M_\odot)>11$) galaxies at $4<z<7$, we consider the systematic effects on the measured photometric redshifts from the introduction of an old and dusty SED template and from the introduction of a bayesian prior taking into account the brightness of the objects, as well as the systematic effects from different star formation histories (SFHs) and from nebular emission lines in the recovery of stellar population parameters. We show that our results are most affected by the bayesian luminosity prior, while nebular emission lines and SFHs only introduce a small dispersion in the measurements. Specifically, the number of $4<z<7$ galaxies ranges from 52 to 382 depending on the adopted configuration. Using these results we investigate, for the first time, the evolution of the massive end of the stellar mass functions (SMFs) at $4<z<7$. Given the rarity of very massive galaxies in the early universe, major contributions to the total error budget come from cosmic variance and poisson noise. The SMF obtained without the introduction of the bayesian luminosity prior does not show any evolution from $z\sim6.5$ to $z\sim 3.5$, implying that massive galaxies could already be present when the Universe was $\sim0.9$~Gyr old. However, the introduction of the bayesian luminosity prior reduces the number of $z>4$ galaxies with best fit masses $\log(M_*/M_\odot)>11$ by 83%, implying a rapid growth of very massive galaxies in the first 1.5 Gyr of cosmic history. From the stellar-mass complete sample, we identify one candidate of a very massive ($\log(M_*/M_\odot)\sim11.5$), quiescent galaxy at $z\sim5.4$, with MIPS $24\mu$m detection suggesting the presence of a powerful obscured AGN.Comment: 23 pages, 18 figures. ApJ accepte

A fast Bayesian approach to discrete object detection in astronomical datasets - PowellSnakes I

A new fast Bayesian approach is introduced for the detection of discrete objects immersed in a diffuse background. This new method, called PowellSnakes, speeds up traditional Bayesian techniques by: i) replacing the standard form of the likelihood for the parameters characterizing the discrete objects by an alternative exact form that is much quicker to evaluate; ii) using a simultaneous multiple minimization code based on Powell's direction set algorithm to locate rapidly the local maxima in the posterior; and iii) deciding whether each located posterior peak corresponds to a real object by performing a Bayesian model selection using an approximate evidence value based on a local Gaussian approximation to the peak. The construction of this Gaussian approximation also provides the covariance matrix of the uncertainties in the derived parameter values for the object in question. This new approach provides a speed up in performance by a factor of `hundreds' as compared to existing Bayesian source extraction methods that use MCMC to explore the parameter space, such as that presented by Hobson & McLachlan. We illustrate the capabilities of the method by applying to some simplified toy models. Furthermore PowellSnakes has the advantage of consistently defining the threshold for acceptance/rejection based on priors which cannot be said of the frequentist methods. We present here the first implementation of this technique (Version-I). Further improvements to this implementation are currently under investigation and will be published shortly. The application of the method to realistic simulated Planck observations will be presented in a forthcoming publication.Comment: 30 pages, 15 figures, revised version with minor changes, accepted for publication in MNRA

Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma

The Hunt for Exomoons with Kepler (HEK): I. Description of a New Observational Project

Two decades ago, empirical evidence concerning the existence and frequency of planets around stars, other than our own, was absent. Since this time, the detection of extrasolar planets from Jupiter-sized to most recently Earth-sized worlds has blossomed and we are finally able to shed light on the plurality of Earth-like, habitable planets in the cosmos. Extrasolar moons may also be frequent habitable worlds but their detection or even systematic pursuit remains lacking in the current literature. Here, we present a description of the first systematic search for extrasolar moons as part of a new observational project called "The Hunt for Exomoons with Kepler" (HEK). The HEK project distills the entire list of known transiting planet candidates found by Kepler (2326 at the time of writing) down to the most promising candidates for hosting a moon. Selected targets are fitted using a multimodal nested sampling algorithm coupled with a planet-with-moon light curve modelling routine. By comparing the Bayesian evidence of a planet-only model to that of a planet-with-moon, the detection process is handled in a Bayesian framework. In the case of null detections, upper limits derived from posteriors marginalised over the entire prior volume will be provided to inform the frequency of large moons around viable planetary hosts, eta-moon. After discussing our methodologies for target selection, modelling, fitting and vetting, we provide two example analyses.Comment: 21 pages, 8 figures, 4 tables, accepted in Ap

Building an Optimal Census of the Solar Neighborhood with Pan-STARRS Data

We estimate the fidelity of solar neighborhood (D < 100 pc) catalogs soon to be derived from Pan-STARRS astrometric data. We explore two quantities used to measure catalog quality: completeness, the fraction of desired sources included in a catalog; and reliability, the fraction of entries corresponding to desired sources. We show that the main challenge in identifying nearby objects with Pan-STARRS will be reliably distinguishing these objects from distant stars, which are vastly more numerous. We explore how joint cuts on proper motion and parallax will impact catalog reliability and completeness. Using synthesized astrometry catalogs, we derive optimum parallax and proper motion cuts to build a census of the solar neighborhood with the Pan-STARRS 3 Pi Survey. Depending on the Galactic latitude, a parallax cut pi / sigma pi > 5 combined with a proper motion cut ranging from mu / sigma mu > 1-8 achieves 99% reliability and 60% completeness.Comment: 7 Pages, 4 Figures, 3 Tables. PASP in pres