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Register a new userMega-Archive and the EURONEAR Tools for Datamining World Astronomical Images
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The world astronomical image archives represent huge opportunities to time-domain astronomy sciences and other hot topics such as space defense, and astronomical observatories should improve this wealth and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and the Mega-Precovery server for data mining images containing Solar system bodies, with focus on near Earth asteroids (NEAs). This paper presents the improvements and introduces some new related data mining tools developed during the last five years. Currently, the Mega-Archive has indexed 15 million images available from six major collections (CADC, ESO, ING, LCOGT, NVO and SMOKA) and other instrument archives and surveys. This meta-data index collection is daily updated (since 2014) by a crawler which performs automated query of five major collections. Since 2016, these data mining tools run to the new dedicated EURONEAR server, and the database migrated to SQL engine which supports robust and fast queries. To constrain the area to search moving or fixed objects in images taken by large mosaic cameras, we built the graphical tools FindCCD and FindCCD for Fixed Objects which overlay the targets across one of seven mosaic cameras (Subaru-SuprimeCam, VST-OmegaCam, INT-WFC, VISTA-VIRCAM, CFHT-MegaCam, Blanco-DECam and Subaru-HSC), also plotting the uncertainty ellipse for poorly observed NEAs. In 2017 we improved Mega-Precovery, which offers now two options for calculus of the ephemerides and three options for the input (objects defined by designation, orbit or observations). Additionally, we developed Mega-Archive for Fixed Objects (MASFO) and Mega-Archive Search for Double Stars (MASDS). We believe that the huge potential of science imaging archives is still insufficiently exploited.
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We describe the contents and functionality of the NASA Exoplanet Archive, a database and tool set funded by NASA to support astronomers in the exoplanet community. The current content of the database includes interactive tables containing properties of all published exoplanets, Kepler planet candidates, threshold-crossing events, data validation reports and target stellar parameters, light curves from the Kepler and CoRoT missions and from several ground-based surveys, and spectra and radial velocity measurements from the literature. Tools provided to work with these data include a transit ephemeris predictor, both for single planets and for observing locations, light curve viewing and normalization utilities, and a periodogram and phased light curve service. The archive can be accessed at http://exoplanetarchive.ipac.caltech.edu.
Astronomical observation data require long-term preservation, and the rapid accumulation of observation data makes it necessary to consider the cost of long-term archive storage. In addition to low-speed disk-based online storage, optical disk or tape-based offline storage can be used to save costs. However, for astronomical research that requires historical data (particularly time-domain astronomy), the performance and energy consumption of data-accessing techniques cause problems because the requested data (which are organized according to observation time) may be located across multiple storage devices. In this study, we design and develop a tool referred to as AstroLayout to redistribute the observation data using spatial aggregation. The core algorithm uses graph partitioning to generate an optimized data placement according to the original observation data statistics and the target storage system. For the given observation data, AstroLayout can copy the long-term archive in the target storage system in accordance with this placement. An efficiency evaluation shows that AstroLayout can reduce the number of devices activated when responding to data-access requests in time-domain astronomy research. In addition to improving the performance of data-accessing techniques, AstroLayout can also reduce the storage systems power consumption. For enhanced adaptability, it supports storage systems of any media, including optical disks, tapes, and hard disks.
We present a new method of interpolation for the pixel brightness estimation in astronomical images. Our new method is simple and easily implementable. We show the comparison of this method with the widely used linear interpolation and other interpolation algorithms using one thousand astronomical images obtained from the Sloan Digital Sky Survey. The comparison shows that our method improves bad pixels brightness estimation with four times lower mean error than the presently most popular linear interpolation and has a better performance than any other examined method. The presented idea is flexible and can be also applied to presently used and future interpolation methods. The proposed method is especially useful for large sky surveys image reduction but can be also applied to single image correction.
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.
Astronomical images from optical photometric surveys are typically contaminated with transient artifacts such as cosmic rays, satellite trails and scattered light. We have developed and tested an algorithm that removes these artifacts using a deep, artifact free, static sky coadd image built up through the median combination of point spread function (PSF) homogenized, overlapping single epoch images. Transient artifacts are detected and masked in each single epoch image through comparison with an artifact free, PSF-matched simulated image that is constructed using the PSF-corrected, model fitting catalog from the artifact free coadd image together with the position variable PSF model of the single epoch image. This approach works well not only for cleaning single epoch images with worse seeing than the PSF homogenized coadd, but also the traditionally much more challenging problem of cleaning single epoch images with better seeing. In addition to masking transient artifacts, we have developed an interpolation approach that uses the local PSF and performs well in removing artifacts whose widths are smaller than the PSF full width at half maximum, including cosmic rays, the peaks of saturated stars and bleed trails. We have tested this algorithm on Dark Energy Survey Science Verification data and present performance metrics. More generally, our algorithm can be applied to any survey which images the same part of the sky multiple times.
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