We present the results of a cross-correlation of the Planck Early Release Compact Source Catalog (ERCSC) with the catalog of Herschel-ATLAS sources detected in the Phase 1 fields, covering 134.55 deg2. There are 28 ERCSC sources detected by Planck at
857 GHz in this area. As many as 16 of them are probably high Galactic latitude cirrus; 10 additional sources can be clearly identified as bright, low-z galaxies; one further source is resolved by Herschel as two relatively bright sources; and the last is resolved into an unusual condensation of low-flux, probably high-redshift point sources, around a strongly lensed Herschel-ATLAS source at z = 3.26. Our results demonstrate that the higher sensitivity and higher angular resolution H-ATLAS maps provide essential information for the interpretation of candidate sources extracted from Planck sub-mm maps.
In this paper we describe the state-of-the art status of multi-frequency detection techniques for compact sources in microwave astronomy. From the simplest cases where the spectral behaviour is well-known (i.e. thermal SZ clusters) to the more comple
x cases where there is little a priori information (i.e. polarized radio sources) we will review the main advances and the most recent results in the detection problem.
The detection and flux estimation of point sources in cosmic microwave background (CMB) maps is a very important task in order to clean the maps and also to obtain relevant astrophysical information. In this paper we propose a maximum a posteriori (M
AP) approach detection method in a Bayesian scheme which incorporates prior information about the source flux distribution, the locations and the number of sources. We apply this method to CMB simulations with the characteristics of the Planck satellite channels at 30, 44, 70 and 100 GHz. With a similar level of spurious sources, our method yields more complete catalogues than the matched filter with a 5 sigma threshold. Besides, the new technique allows us to fix the number of detected sources in a non-arbitrary way.
We propose a Bayesian approach to joint source separation and restoration for astrophysical diffuse sources. We constitute a prior statistical model for the source images by using their gradient maps. We assume a t-distribution for the gradient maps
in different directions, because it is able to fit both smooth and sparse data. A Monte Carlo technique, called Langevin sampler, is used to estimate the source images and all the model parameters are estimated by using deterministic techniques.
We propose to model the image differentials of astrophysical source maps by Students t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix
the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Students t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.
We aim to present a tutorial on the detection, parameter estimation and statistical analysis of compact sources (far galaxies, galaxy clusters and Galactic dense emission regions) in cosmic microwave background observations. The topic is of great rel
evance for current and future cosmic microwave background missions because the presence of compact sources in the data introduces very significant biases in the determination of the cosmological parameters that determine the energy contain, origin and evolution of the universe and because compact sources themselves provide us with important information about the large scale structure of the universe.
We consider a set of M images, whose pixel intensities at a common point can be treated as the components of a M-dimensional vector. We are interested in the estimation of the modulus of such a vector associated to a compact source. For instance, the
detection/estimation of the polarized signal of compact sources immersed in a noisy background is relevant in some fields like Astrophysics. We develop two different techniques, one based on the Maximum Likelihood Estimator (MLE) applied to the modulus distribution, the modulus filter (ModF) and other based on prefiltering the components before fusion, the filtered fusion (FF), to deal with this problem. We present both methods in the general case of M images and apply them to the particular case of three images (linear plus circular polarization). Numerical simulations have been performed to test these filters considering polarized compact sources immersed in stationary noise. The FF performs better than the ModF in terms of errors in the estimated amplitude and position of the source, especially in the low signal-to-noise case. We also compare both methods with the direct application of a matched filter (MF) on the polarization data. This last technique is clearly outperformed by the new methods.
In this paper we introduce a new linear filtering technique, the so-called matrix filters, that maximizes the signal-to-interference ratio of compact sources of unknown intensity embedded in a set of images by taking into account the cross-correlatio
ns between the different channels. By construction, the new filtering technique outperforms (or at least equals) the standard matched filter applied on individual images. An immediate application is the detection of extragalactic point sources in Cosmic Microwave Background images obtained at different wavelengths. We test the new technique in two simulated cases: a simple two-channel case with ideal correlated color noise and more realistic simulations of the sky as it will be observed by the LFI instrument of the upcoming ESAs Planck mission. In both cases we observe an improvement with respect to the standard matched filter in terms of signal-to-noise interference, number of detections and number of false alarms.
