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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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