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| Paper: |
The Design Strategy of Scientific Data Quality Control Software for Euclid Mission. |
| Volume: |
521, Astronomical Data Analysis Software and Systems XXVI |
| Page: |
228 |
| Authors: |
Brescia, M.; Cavuoti, S.; Fredvik, T.; Haugan, S. V. H.; Gozaliasl, G.; Kirkpatrick, C.; Kurki-Suonio, H.; Longo, G.; Nilsson, K.; Wiesmann, M. |
| Abstract: |
The most valuable asset of a space mission like Euclid are the data.
Due to their huge volume, the automatic quality control becomes a
crucial aspect over the entire lifetime of the experiment. Here we
focus on the design strategy for the Science Ground Segment (SGS) Data
Quality Common Tools (DQCT), which has the main role to provide
software solutions to gather, evaluate, and record quality information
about the raw and derived data products from a primarily scientific
perspective. The stakeholders for this system include Consortium
scientists, users of the science data, and the ground segment data
management system itself. The SGS DQCT will provide a quantitative
basis for evaluating the application of reduction and calibration
reference data (flat-fields, linearity correction, reference catalogs,
etc.), as well as diagnostic tools for quality parameters, flags,
trend analysis diagrams and any other metadata parameter produced by
the pipeline, collected in incremental quality reports specific to
each data level and stored on the Euclid Archive during pipeline
processing. In a large programme like Euclid, it is prohibitively
expensive to process large amount of data at the pixel level just for
the purpose of quality evaluation. Thus, all measures of quality at
the pixel level are implemented in the individual pipeline stages, and
passed along as metadata in the production. In this sense most of the
tasks related to science data quality are delegated to the pipeline
stages, even though the responsibility for science data quality is
managed at a higher level. The DQCT subsystem of the SGS is currently
under development, but its path to full realization will likely be
different than that of other subsystem; primarily because, due to a
high level of parallelism and to the wide pipeline processing
redundancy (for instance the mechanism of double Science Data Center
for each processing function) the data quality tools have not only to
be widely spread over all pipeline segments and data levels, but also
to minimize the occurrences of potential diversity of solutions
implemented for similar functions, ensuring the maximum of coherency
and standardization for quality evaluation and reporting in the SGS. |
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