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One of the biggest problems faced by current and next-generation astronomical surveys is trying to produce large numbers of accurate cross identifications across a range of wavelength regimes with varying data quality and positional uncertainty. Until recently simple spatial nearest neighbour associations have been sufficient for most applications. However as advances in instrumentation allow more sensitive images to be made the rapid increase in the source density has meant that source confusion across multiple wavelengths is a serious problem. The field of far-IR and sub-mm astronomy has been particularly hampered by such problems. The poor angular resolution of current sub-mm and far-IR instruments is such that in a lot of cases there are multiple plausible counterparts for each source at other wavelengths. Here we present a new automated method of producing associations between sources at different wavelengths using a combination of spatial and SED information set in the Bayesian framework presented by Budavari & Szalay (2008). Testing of the technique is performed on both simulated catalogues of sources from GaLICS and real data from multi-wavelength observations of the SXDF. It is found that a single figure of merit, the Bayes factor, can be effectively used to describe the confidence in the match. Further applications of this technique to future Herschel datasets are discussed.
Context:Blazars are the rarest and most powerful active galactic nuclei, playing a crucial and growing role in today multi-frequency and multi-messenger astrophysics. Current blazar catalogs, however, are incomplete and particularly depleted at low G
We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio measurements. The method consists of (1)
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A new approach is given for the implementation of boundary conditions used in solving the Mukhanov-Sasaki equation in the context of inflation. The familiar quantization procedure is reviewed, along with a discussion of where one might expect deviati
Cosmological surveys in the far infrared are known to suffer from confusion. The Bayesian de-blending tool, XID+, currently provides one of the best ways to de-confuse deep Herschel SPIRE images, using a flat flux density prior. This work is to demon