The Bayesian Technique for Multi-image Analysis (BaTMAn) is 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). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise.
BaTMAn has been designed to identify (and keep) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). At variance with other tessellation schemes, the main aim of the algorithm is not to automatically improve the signal-to-noise ratio, but to characterize spatially-resolved data prior to their analysis.
A thorough description of the algorithm and some test cases can be found in Casado et al. (2017; MNRAS, in press; arXiv:1607.07299). You are welcome to use it for your own purposes as long as you cite this paper and provide a link to this web page. We also encourage you to contact us in case you have any questions and/or interest in adapting/enhancing BaTMAn to work within your own problem/analysis pipeline.
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