BaTMAn: Bayesian Technique for Multi-image Analysis
  This paper describes the Bayesian Technique for Multi-image Analysis
(BaTMAn), a novel image-segmentation technique based on Bayesian statistics
that characterizes any astronomical dataset containing spatial information and
performs a tessellation based on the measurements and errors provided as input.
The algorithm iteratively merges spatial elements as long as they are
statistically consistent with carrying the same information (i.e. identical
signal within the errors). We illustrate its operation and performance with a
set of test cases including both synthetic and real Integral-Field
Spectroscopic data. The output segmentations adapt to the underlying spatial
structure, regardless of its morphology and/or the statistical properties of
the noise. The quality of the recovered signal represents an improvement with
respect to the input, especially in regions with low signal-to-noise ratio.
However, the algorithm may be sensitive to small-scale random fluctuations, and
its performance in presence of spatial gradients is limited. Due to these
effects, errors may be underestimated by as much as a factor of two. Our
analysis reveals that the algorithm prioritizes conservation of all the
statistically-significant information over noise reduction, and that the
precise choice of the input data has a crucial impact on the results. Hence,
the philosophy of BaTMAn is not to be used as a `black box' to improve the
signal-to-noise ratio, but as a new approach to characterize spatially-resolved
data prior to its analysis. The source code is publicly available at
http://astro.ft.uam.es/SELGIFS/BaTMAn .
