English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/151929
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:
Title

BATMAN: Bayesian Technique for Multi-image Analysis

AuthorsCasado, J.; Ascasibar, Y.; García-Benito, Rubén ; Guidi, G.; Choudhury, O. S.; Bellocchi, E.; Sánchez, Sebastián F. ; Diaz, A. I.
KeywordsMethods: data analysis
Methods: numerical
Methods: statistical
Techniques: image processing
Issue Date28-Dec-2016
PublisherOxford University Press
CitationMonthly Notices of the Royal Astronomical Society 466(4): 3989-4008 ( (2017)
AbstractThis paper describes the Bayesian Technique for Multi-image Analysis (BATMAN), a novel image-segmentation technique based on Bayesian statistics that characterizes any astronomical data set 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 algorithmmay 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 2. 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.
Publisher version (URL)http://dx.doi.org/10.1093/mnras/stw3362
URIhttp://hdl.handle.net/10261/151929
DOI10.1093/mnras/stw3362
ISSN0035-8711
E-ISSN1365-2966
Appears in Collections:(IAA) Artículos
Files in This Item:
File Description SizeFormat 
IAA_2017_MNRASstw3362.pdf8,82 MBAdobe PDFThumbnail
View/Open
Show full item record
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.