BaTMAn: Bayesian Technique for Multi-image Analysis

TL;DR

BaTMAn is a Bayesian image segmentation method for astronomical data that adaptively characterizes spatial structures, improving signal recovery in low S/N regions while emphasizing the importance of input data quality.

Contribution

It introduces a novel Bayesian tessellation algorithm for multi-image analysis that adapts to spatial structures and emphasizes data characterization over noise reduction.

Findings

Improves signal recovery in low S/N regions.

Adapts to various spatial morphologies.

Sensitive to small-scale fluctuations and spatial gradients.

Abstract

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 .