GMOSS: All-sky model of spectral radio brightness based on physical components and associated radiative processes

We present Global MOdel for the radio Sky Spectrum (GMOSS) -- a novel, physically motivated model of the low-frequency radio sky from 22 MHz to 23 GHz. GMOSS invokes different physical components and associated radiative processes to describe the sky spectrum over 3072 pixels of $5^{\circ}$ resolution. The spectra are allowed to be convex, concave or of more complex form with contributions from synchrotron emission, thermal emission and free-free absorption included. Physical parameters that describe the model are optimized to best fit four all-sky maps at 150 MHz, 408 MHz, 1420 MHz and 23 GHz and two maps at 22 MHz and 45 MHz generated using the Global Sky Model of de Oliveira-Costa et al. (2008). The fractional deviation of model to data has a median value of $6\%$ and is less than $17\%$ for $99\%$ of the pixels. Though aimed at modeling of foregrounds for the global signal arising from the redshifted 21-cm line of Hydrogen during Cosmic Dawn and Epoch of Reionization (EoR) - over redshifts $150\lesssim z \lesssim 6$, GMOSS is well suited for any application that requires simulating spectra of the low-frequency radio sky as would be observed by the beam of any instrument. The complexity in spectral structure that naturally arises from the underlying physics of the model provides a useful expectation for departures from smoothness in EoR foreground spectra and hence may guide the development of algorithms for EoR signal detection. This aspect is further explored in a subsequent paper.Comment: 19 pages, 7 figure

Constraining Galactic dark matter with gamma-ray pixel counts statistics

Gamma-ray searches for new physics such as dark matter are often driven by investigating the composition of the extragalactic gamma-ray background (EGB). Classic approaches to EGB decomposition manifest in resolving individual point sources and dissecting the intensity spectrum of the remaining unresolved component. Furthermore, statistical methods have recently been proven to outperform the sensitivity of classic source detection algorithms in finding point-source populations in the unresolved flux regime. In this article, we employ the 1-point photon count statistics of eight years of Fermi-LAT data to resolve the population of extragalactic point sources and to decompose the diffuse isotropic background contribution for Galactic latitudes |b|>30 deg. We use three adjacent energy bins between 1 and 10 GeV. For the first time, we extend the analysis to incorporate a potential contribution from annihilating dark matter smoothly distributed in the Galaxy. We investigate the sensitivity reach of 1-point statistics for constraining the thermally-averaged self-annihilation cross section of dark matter, using different template models for the Galactic foreground emission. Given the official Fermi-LAT interstellar emission model, we set upper bounds on the DM self-annihilation cross section that are comparable with the constraints obtained by other indirect detection methods, in particular by the stacking analysis of several dwarf spheroidal galaxies.Comment: 11 pages, 7 figures, 1 table; v2: major changes improving the selection of the RO

Localizing Region-Based Active Contours

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.2004611In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models

Reionization and Cosmology with 21 cm Fluctuations

Measurement of the spatial distribution of neutral hydrogen via the redshifted 21 cm line promises to revolutionize our knowledge of the epoch of reionization and the first galaxies, and may provide a powerful new tool for observational cosmology from redshifts 1<z<4 . In this review we discuss recent advances in our theoretical understanding of the epoch of reionization (EoR), the application of 21 cm tomography to cosmology and measurements of the dark energy equation of state after reionization, and the instrumentation and observational techniques shared by 21 cm EoR and post reionization cosmology machines. We place particular emphasis on the expected signal and observational capabilities of first generation 21 cm fluctuation instruments.Comment: Invited review for Annual Review of Astronomy and Astrophysics (2010 volume

Robust Foregrounds Removal for 21-cm Experiments

Direct detection of the Epoch of Reionization via the redshifted 21-cm line will have unprecedented implications on the study of structure formation in the early Universe. To fulfill this promise current and future 21-cm experiments will need to detect the weak 21-cm signal over foregrounds several order of magnitude greater. This requires accurate modeling of the galactic and extragalactic emission and of its contaminants due to instrument chromaticity, ionosphere and imperfect calibration. To solve for this complex modeling, we propose a new method based on Gaussian Process Regression (GPR) which is able to cleanly separate the cosmological signal from most of the foregrounds contaminants. We also propose a new imaging method based on a maximum likelihood framework which solves for the interferometric equation directly on the sphere. Using this method, chromatic effects causing the so-called "wedge" are effectively eliminated (i.e. deconvolved) in the cylindrical ($k_{\perp}, k_{\parallel}$) power spectrum.Comment: Subbmited to the Proceedings of the IAUS333, Peering Towards Cosmic Dawn, 4 pages, 2 figure

Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals

This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes. The setting of this problem is fully unsupervised, without even image-level annotations or any assumption of a single dominant class. This is far more general than typical colocalization, cosegmentation, or weakly-supervised localization tasks. We tackle the discovery and localization problem using a part-based region matching approach: We use off-the-shelf region proposals to form a set of candidate bounding boxes for objects and object parts. These regions are efficiently matched across images using a probabilistic Hough transform that evaluates the confidence for each candidate correspondence considering both appearance and spatial consistency. Dominant objects are discovered and localized by comparing the scores of candidate regions and selecting those that stand out over other regions containing them. Extensive experimental evaluations on standard benchmarks demonstrate that the proposed approach significantly outperforms the current state of the art in colocalization, and achieves robust object discovery in challenging mixed-class datasets.Comment: CVPR 201

Video foreground detection based on symmetric alpha-stable mixture models.

Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the camera remains stationary and there exist no movements in the background. These assumptions restrict the applicability of BS methods to real-time object detection in video. In this paper, we propose an extended cluster BS technique with a mixture of symmetric alpha stable (SS) distributions. An on-line self-adaptive mechanism is presented that allows automated estimation of the model parameters using the log moment method. Results over real video sequences from indoor and outdoor environments, with data from static and moving video cameras are presented. The SS mixture model is shown to improve the detection performance compared with a cluster BS method using a Gaussian mixture model and the method of Li et al. [11]