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Title:Alma data mining toolkit
Author(s):Friedel, Douglas
Contributor(s):Xu, Lisa; Harris, Robert J; Mundy, Lee; Rauch, Kevin P.; Pound, Marc W.; Teuben, Peter J.; Looney, Leslie
Abstract:ADMIT (ALMA Data Mining Toolkit) is a Python based pipeline toolkit for the creation and analysis of new science products from ALMA data. ADMIT quickly provides users with a detailed overview of their science products, for example: line identifications, line 'cutout' cubes, moment maps, and emission type analysis (e.g., feature detection). Users can download the small ADMIT pipeline product ($<$ 20MB), analyze the results, then fine-tune and re-run the ADMIT pipeline (or any part thereof) on their own machines and interactively inspect the results. ADMIT has both a web browser and command line interface available for this purpose. By analyzing multiple data cubes simultaneously, data mining between many astronomical sources and line transitions are possible. Users are also able to enhance the capabilities of ADMIT by creating customized ADMIT tasks satisfying any special processing needs. We will present some of the salient features of ADMIT and example use cases.
Issue Date:2016-06-23
Publisher:International Symposium on Molecular Spectroscopy
Genre:Conference Paper/Presentation
Rights Information:Copyright 2016 by the authors
Date Available in IDEALS:2016-08-22

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