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Paper: ADMIT: The ALMA Data Mining Toolkit
Volume: 495, Astronomical Data Analysis Software and Systems XXIV (ADASS XXIV)
Page: 305
Authors: Teuben, P.; Pound, M.; Mundy, L.; Rauch, K.; Friedel, D.; Looney, L.; Xu, L.; Kern, J.
Abstract: ADMIT (ALMA Data Mining ToolkiT), a toolkit for the creation of new science products from ALMA data, is being developed as an ALMA Development Project. It is written in Python and, while specifically targeted for a uniform analysis of the ALMA science products that come out of the ALMA pipeline, it is designed to be generally applicable to (radio) astronomical data. It first provides users with a detailed view of their science products created by ADMIT inside the ALMA pipeline: line identifications, line ‘cutout' cubes, moment maps, emission type analysis (e.g., feature detection). Using descriptor vectors the ALMA data archive is enriched with useful information to make archive data mining possible. Users can also opt to download the (small) ADMIT pipeline product, then fine-tune and re-run the pipeline and inspect their hopefully improved data. By running many projects in a parallel fashion, data mining between many astronomical sources and line transitions will also be possible. Future implementations of ADMIT may include EVLA and other instruments.
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