Bayesian methods of astronomical source extraction

We present two new source extraction methods, based on Bayesian model selection and using the Bayesian Information Criterion (BIC). The first is a source detection filter, able to simultaneously detect point sources and estimate the image background. The second is an advanced photometry technique, which measures the flux, position (to sub-pixel accuracy), local background and point spread function. We apply the source detection filter to simulated Herschel-SPIRE data and show the filter's ability to both detect point sources and also simultaneously estimate the image background. We use the photometry method to analyse a simple simulated image containing a source of unknown flux, position and point spread function; we not only accurately measure these parameters, but also determine their uncertainties (using Markov-Chain Monte Carlo sampling). The method also characterises the nature of the source (distinguishing between a point source and extended source). We demonstrate the effect of including additional prior knowledge. Prior knowledge of the point spread function increase the precision of the flux measurement, while prior knowledge of the background has onlya small impact. In the presence of higher noise levels, we show that prior positional knowledge (such as might arise from a strong detection in another waveband) allows us to accurately measure the source flux even when the source is too faint to be detected directly. These methods are incorporated in SUSSEXtractor, the source extraction pipeline for the forthcoming Akari FIS far-infrared all-sky survey. They are also implemented in a stand-alone, beta-version public tool that can be obtained at http://astronomy.sussex.ac.uk/$\sim$rss23/sourceMiner\_v0.1.2.0.tar.gzComment: Accepted for publication by ApJ (this version compiled used emulateapj.cls

PynPoint: a modular pipeline architecture for processing and analysis of high-contrast imaging data

The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and PSF subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. We aim at developing a generic and modular data reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and in particular well suitable for the 3-5 micron wavelength range where typically (ten) thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. PynPoint is written in Python 2.7 and applies various image processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF subtraction tool based on PCA. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L' and M' data of beta Pic b and reassessed the planet's brightness and position with an MCMC analysis, and we provide a derivation of the photometric error budget.Comment: 16 pages, 9 figures, accepted for publication in A&A, PynPoint is available at https://github.com/PynPoint/PynPoin

Can Chandra resolve the remaining cosmic X-ray background?

The deepest extragalactic X-ray observation, the 2 Ms Chandra Deep Field North (CDF-N), resolves ~80% of the total extragalactic cosmic X-ray background (CXB) in the 1-2 keV band. Recent work has shown that 70% of the remaining CXB flux is associated with sources detected by the Hubble Space Telescope (HST). This paper uses the existing CDF-N data to constrain the X-ray flux distribution of these X-ray undetected HST sources, by comparing the number of 0.5-2 keV X-ray counts at the HST positions to those expected for model flux distributions. In the simple case where all the undetected HST X-ray sources have the same 0.5-2 keV flux, the data are best fit by 1.5-3 counts per source in 2 Ms, compared to a detection limit (at 10% completeness) of 9 counts. Assuming a more realistic power-law logN-logS distribution [N(>S) S^-alpha], the data favor a relatively steep flux distribution, with alpha=1.1^+0.5_-0.3 (limits are 99% confidence). This slope is very similar to that previously found for faint normal and starburst galaxies in the CDF-N. These results suggest deeper Chandra observations will detect a new population of faint X-ray sources, but extremely deep exposures are needed to resolve the remainder of the soft CXB. In the most optimistic scenario, when the HST sources have the flattest allowed flux distribution and all the sources without HST counterparts are detected, observations 5 times more sensitive than the existing ones would resolve at most ~60% of the remaining soft CXB.Comment: 9 emulateapj pages, 8 figures, v3: matches version to appear in ApJ (note correction to approximation of Poisson errors

The Bolocam Galactic Plane Survey IX: Data Release 2 and Outer Galaxy Extension

We present a re-reduction and expansion of the Bolocam Galactic Plane Survey, first presented by Aguirre et al. (2011) and Rosolowsky et al. (2010). The BGPS is a 1.1 mm survey of dust emission in the Northern galactic plane, covering longitudes -10 < \ell < 90 and latitudes |b| < 0.5 with a typical 1-\sigma RMS sensitivity of 30-100 mJy in a 33" beam. Version 2 of the survey includes an additional 20 square degrees of coverage in the 3rd and 4th quadrants and 2 square degrees in the 1st quadrant. The new data release has improved angular recovery, with complete recovery out to 80" and partial recovery to 300", and reduced negative bowls around bright sources resulting from the atmospheric subtraction process. We resolve the factor of 1.5 flux calibration offset between the v1.0 data release and other data sets and determine that there is no offset between v2.0 and other data sets. The v2.0 pointing accuracy is tested against other surveys and demonstrated to be accurate and an improvement over v1.0. We present simulations and tests of the pipeline and its properties, including measurements of the pipeline's angular transfer function. The Bolocat cataloging tool was used to extract a new catalog, which includes 8594 sources, with 591 in the expanded regions. We have demonstrated that the Bolocat 40" and 80" apertures are accurate even in the presence of strong extended background emission. The number of sources is lower than in v1.0, but the amount of flux and area included in identified sources is larger.Comment: 36 pages, 16 figures, accepted to ApJS. Data available from http://irsa.ipac.caltech.edu/data/BOLOCAM_GPS

The Deep Diffuse Extragalactic Radio Sky at 1.75 GHz

We present a study of diffuse extragalactic radio emission at $1.75\,$GHz from part of the ELAIS-S1 field using the Australia Telescope Compact Array. The resulting mosaic is $2.46\,$deg$^2$, with a roughly constant noise region of $0.61\,$deg$^2$ used for analysis. The image has a beam size of $150 \times60\,$arcsec and instrumental $\langle\sigma_{\rm n}\rangle= (52\pm5)\, \mu$Jy beam$^{-1}$. Using point-source models from the ATLAS survey, we subtract the discrete emission in this field for $S \ge 150\, \mu$Jy beam$^{-1}$. Comparison of the source-subtracted probability distribution, or \pd, with the predicted distribution from unsubtracted discrete emission and noise, yields an excess of $(76 \pm 23) \, \mu$Jy beam$^{-1}$. Taking this as an upper limit on any extended emission we constrain several models of extended source counts, assuming $\Omega_{\rm source} \le 2\,$arcmin. The best-fitting models yield temperatures of the radio background from extended emission of $T_{\rm b}=(10\pm7) \,$mK, giving an upper limit on the total temperature at $1.75\,$GHz of $(73\pm10)\,$mK. Further modelling shows that our data are inconsistent with the reported excess temperature of ARCADE2 to a source-count limit of $1\, \mu$Jy. Our new data close a loop-hole in the previous constraints, because of the possibility of extended emission being resolved out at higher resolution. Additionally, we look at a model of cluster halo emission and two WIMP dark matter annihilation source-count models, and discuss general constraints on any predicted counts from such sources. Finally, we report the derived integral count at $1.4\,$GHz using the deepest discrete count plus our new extended-emission limits, providing numbers that can be used for planning future ultra-deep surveys.Comment: 18 pages, 15 figures, 7 tables, Accepted by MNRA

A re-analysis of the three-year WMAP temperature power spectrum and likelihood

We analyze the three-year WMAP temperature anisotropy data seeking to confirm the power spectrum and likelihoods published by the WMAP team. We apply five independent implementations of four algorithms to the power spectrum estimation and two implementations to the parameter estimation. Our single most important result is that we broadly confirm the WMAP power spectrum and analysis. Still, we do find two small but potentially important discrepancies: On large angular scales there is a small power excess in the WMAP spectrum (5-10% at l<~30) primarily due to likelihood approximation issues between 13 <= l <~30. On small angular scales there is a systematic difference between the V- and W-band spectra (few percent at l>~300). Recently, the latter discrepancy was explained by Huffenberger et al. (2006) in terms of over-subtraction of unresolved point sources. As far as the low-l bias is concerned, most parameters are affected by a few tenths of a sigma. The most important effect is seen in n_s. For the combination of WMAP, Acbar and BOOMERanG, the significance of n_s =/ 1 drops from ~2.7 sigma to ~2.3 sigma when correcting for this bias. We propose a few simple improvements to the low-l WMAP likelihood code, and introduce two important extensions to the Gibbs sampling method that allows for proper sampling of the low signal-to-noise regime. Finally, we make the products from the Gibbs sampling analysis publically available, thereby providing a fast and simple route to the exact likelihood without the need of expensive matrix inversions.Comment: 14 pages, 7 figures. Accepted for publication in ApJ. Numerical results unchanged, but interpretation sharpened: Likelihood approximation issues at l=13-30 far more important than potential foreground issues at l <= 12. Gibbs products (spectrum and sky samples, and "easy-to-use" likelihood module) available from http://www.astro.uio.no/~hke/ under "Research