A Bayesian approach to discrete object detection in astronomical datasets

Details A Bayesian approach to discrete object detection in astronomical datasets A Bayesian approach to discrete object detection in astronomical datasets

2002-04-26

arxiv.org/abs/astro-ph/0204457v2

Mon.Not.Roy.Astron.Soc. 338 (2003) 765

Astronomy and Astrophysics

Astrophysics

20 pages, 12 figures, accepted by MNRAS; contains some additional material in response to referee's comments

Scientific paper

10.1046/j.1365-8711.2003.06094.x

A Bayesian approach is presented for detecting and characterising the signal from discrete objects embedded in a diffuse background. The approach centres around the evaluation of the posterior distribution for the parameters of the discrete objects, given the observed data, and defines the theoretically-optimal procedure for parametrised object detection. Two alternative strategies are investigated: the simultaneous detection of all the discrete objects in the dataset, and the iterative detection of objects. In both cases, the parameter space characterising the object(s) is explored using Markov-Chain Monte-Carlo sampling. For the iterative detection of objects, another approach is to locate the global maximum of the posterior at each iteration using a simulated annealing downhill simplex algorithm. The techniques are applied to a two-dimensional toy problem consisting of Gaussian objects embedded in uncorrelated pixel noise. A cosmological illustration of the iterative approach is also presented, in which the thermal and kinetic Sunyaev-Zel'dovich effects from clusters of galaxies are detected in microwave maps dominated by emission from primordial cosmic microwave background anisotropies.

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