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We describe the WFCAM Science Archive (WSA), which is the primary point of access for users of data from the wide-field infrared camera WFCAM on the United Kingdom Infrared Telescope (UKIRT), especially science catalogue products from the UKIRT Infrared Deep Sky Survey (UKIDSS). We describe the database design with emphasis on those aspects of the system that enable users to fully exploit the survey datasets in a variety of different ways. We give details of the database-driven curation applications that take data from the standard nightly pipeline-processed and calibrated files for the production of science-ready survey datasets. We describe the fundamentals of querying relational databases with a set of astronomy usage examples, and illustrate the results.
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We describe the VISTA Science Archive (VSA) and its first public release of data from five of the six VISTA Public Surveys. The VSA exists to support the VISTA Surveys through their lifecycle: the VISTA Public Survey consortia can use it during their quality control assessment of survey data products before submission to the ESO Science Archive Facility (ESO SAF); it supports their exploitation of survey data prior to its publication through the ESO SAF; and, subsequently, it provides the wider community with survey science exploitation tools that complement the data product repository functionality of the ESO SAF. This paper has been written in conjunction with the first public release of public survey data through the VSA and is designed to help its users understand the data products available and how the functionality of the VSA supports their varied science goals. We describe the design of the database and outline the database-driven curation processes that take data from nightly pipeline-processed and calibrated FITS files to create science-ready survey datasets. Much of this design, and the codebase implementing it, derives from our earlier WFCAM Science Archive (WSA), so this paper concentrates on the VISTA-specific aspects and on improvements made to the system in the light of experience gained in operating the WSA.
In December 2016, the Atacama Large Millimeter/submillimeter Array (ALMA) carried out the first regular observations of the Sun. These early observations and the reduction of the respective data posed a challenge due to the novelty and complexity of observing the Sun with ALMA. The difficulties with producing science-ready time-resolved imaging products in a format familiar and usable by solar physicists based on the measurement sets delivered by ALMA had so far limited the availability of such data. With the development of the Solar ALMA Pipeline (SoAP), it has now become possible to routinely reduce such data sets. As a result, a growing number of science-ready solar ALMA datasets is now offered in the form of Solar ALMA Science Archive (SALSA). So far, SALSA contains primarily time series of single-pointing interferometric images at cadences of one or two seconds. The data arrays are provided in FITS format. We also present the first version of a standardised header format that accommodates future expansions and fits within the scope of other standards including the ALMA Science Archive itself and SOLARNET. The headers also include information designed to aid the reproduction of the imaging products from the raw data. Links to co-observations, if available, with a focus on those of the Interface Region Imaging Spectrograph (IRIS), are also provided. SALSA is accompanied by the Solar ALMA Library of Auxiliary Tools (SALAT) that contains IDL and Python routines for convenient loading and quick-look analysis of SALSA data.
We announce the public release of 141,531 moderate-dispersion optical spectra of 72,247 objects acquired over the past 25 years with the FAST Spectrograph on the Fred L. Whipple Observatory 1.5-meter Tillinghast telescope. We describe the data acquisition and processing so that scientists can understand the spectra. We highlight some of the largest FAST survey programs, and make recommendations for use. The spectra have been placed in a Virtual Observatory accessible archive and are ready for download.
The Additional Representative Images for Legacy (ARI-L) project is a European Development project for ALMA Upgrade approved by the Joint ALMA Observatory (JAO) and the European Southern Observatory (ESO), started in June 2019. It aims to increase the legacy value of the ALMA Science Archive (ASA) by bringing the reduction level of ALMA data from Cycles 2-4 close to that of data from more recent Cycles processed for imaging with the ALMA Pipeline. As of mid-2021 more than 150000 images have been returned to the ASA for public use. At its completion in 2022, the project will have provided enhanced products for at least 70% of the observational data from Cycles 2-4 processable with the ALMA Pipeline. In this paper we present the project rationale, its implementation, and the new opportunities offered to ASA users by the ARI-L products. The ARI-L cubes and images complement the much limited number of archival image products generated during the data quality assurance stages (QA2), which cover only a small fraction of the available data for those Cycles. ARI-L imaging products are highly relevant for many science cases and significantly enhance the possibilities for exploiting archival data. Indeed, ARI-L products facilitate archive access and data usage for science purposes even for non-expert data miners, provide a homogeneous view of all data for better dataset comparisons and download selections, make the archive more accessible to visualization and analysis tools, and enable the generation of preview images and plots similar to those possible for subsequent Cycles.
Stellar variability in the near-infrared (NIR) remains largely unexplored. The exploitation of public science archives with data-mining methods offers a perspective for the time-domain exploration of the NIR sky. We perform a comprehensive search for stellar variability using the optical-NIR multi-band photometric data in the public Calibration Database of the WFCAM Science Archive (WSA), with the aim of contributing to the general census of variable stars, and to extend the current scarce inventory of accurate NIR light curves for a number of variable star classes. We introduce new variability indices designed for multi-band data with correlated sampling, and apply them for pre-selecting variable star candidates, i.e., light curves that are dominated by correlated variations, from noise-dominated ones. Pre-selection criteria are established by robust numerical tests for evaluating the response of variability indices to colored noise characteristic to the data. We find 275 periodic variable stars and an additional 44 objects with suspected variability with uncertain periods or apparently aperiodic variation. Only 44 of these objects had been previously known, including 11 RR~Lyrae stars in the outskirts of the globular cluster M3 (NGC~5272). We provide a preliminary classification of the new variable stars that have well-measured light curves, but the variability types of a large number of objects remain ambiguous. We classify most of the new variables as contact binary stars, but we also find several pulsating stars, among which 34 are probably new field RR~Lyrae and 3 are likely Cepheids. We also identify 32 highly reddened variable objects close to previously known dark nebulae, suggesting that these are embedded young stellar objects. We publish our results and all light-curve data as the WFCAM Variable Star Catalog.
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