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Paper: The ALMA Data Mining Toolkit I: Archive Setup and User Usage
Volume: 485, Astronomical Data Analysis Software and Systems XXIII
Page: 147
Authors: Teuben, P.; Pound, M.; Mundy, L.; Looney, L.; Friedel, D. N.
Abstract: We report on an ALMA development study and project where we employ a novel approach to add data and data descriptors to ALMA archive data and allowing further flexible data mining on retrieved data. We call our toolkit ADMIT (the ALMA Data Mining Toolkit) that works within the Python based CASA environment. What is described here is a design study, with some exiting toy code to prove the concept. After ingestion of science ready datacubes, ADMIT will compute a number of basic and advanced data products, and their descriptors. Examples of such data products are cube statistics, line identification tables, line cubes, moment maps, an integrated spectrum, overlap integrals and feature extraction tables. Together with a descriptive XML file, a small number of visual aids are added to a ZIP file that is deposited into the archive. Large datasets (such as line cubes) will have to be rederived by the user once they have also downloaded the actual ALMA Data Products, or via VO services if available. ADMIT enables the user to rederive all its products with different methods and parameters, and compare archive product with their own.
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