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Register a new userMining the Kilo-Degree Survey for solar system objects
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The search for minor bodies in the solar system promises insights into its formation history. Wide imaging surveys offer the opportunity to serendipitously discover and identify these traces of planetary formation and evolution. We aim to present a method to acquire position, photometry, and proper motion measurements of solar system objects in surveys using dithered image sequences. The application of this method on the Kilo-Degree Survey is demonstrated. Optical images of 346 square degree fields of the sky are searched in up to four filters using the AstrOmatic software suite to reduce the pixel to catalog data. The solar system objects within the acquired sources are selected based on a set of criteria depending on their number of observation, motion, and size. The Virtual Observatory SkyBoT tool is used to identify known objects. We observed 20,221 SSO candidates, with an estimated false-positive content of less than 0.05%. Of these SSO candidates, 53.4% are identified by SkyBoT. KiDS can detect previously unknown SSOs because of its depth and coverage at high ecliptic latitude, including parts of the Southern Hemisphere. Thus we expect the large fraction of the 46.6% of unidentified objects to be truly new SSOs. Our method is applicable to a variety of dithered surveys such as DES, LSST, and Euclid. It offers a quick and easy-to-implement search for solar system objects. SkyBoT can then be used to estimate the completeness of the recovered sample.
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The Kilo Degree Survey (KiDS) is a 1500 square degree optical imaging survey with the recently commissioned OmegaCAM wide-field imager on the VLT Survey Telescope (VST). A suite of data products will be delivered to the European Southern Observatory (ESO) and the community by the KiDS survey team. Spread over Europe, the KiDS team uses Astro-WISE to collaborate efficiently and pool hardware resources. In Astro-WISE the team shares, calibrates and archives all survey data. The data-centric architectural design realizes a dynamic live archive in which new KiDS survey products of improved quality can be shared with the team and eventually the full astronomical community in a flexible and controllable manner.
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14 spectroscopic data. We first cleaned the input KiDS data from entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multi-dimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r<22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190,000 quasar candidates. Accuracy of 97%, purity of 91%, and completeness of 87%, as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from WISE. An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of p(QSO)>0.8 is optimal for purity, whereas p(QSO)>0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.
In the preparation for ESAs Euclid mission and the large amount of data it will produce, we train deep convolutional neural networks on Euclid simulations classify solar system objects from other astronomical sources. Using transfer learning we are able to achieve a good performance despite our tiny dataset with as few as 7512 images. Our best model correctly identifies objects with a top accuracy of 94% and improves to 96% when Euclids dither information is included. The neural network misses ~50% of the slowest moving asteroids (v < 10 arcsec/h) but is otherwise able to correctly classify asteroids even down to 26 mag. We show that the same model also performs well at classifying stars, galaxies and cosmic rays, and could potentially be applied to distinguish all types of objects in the Euclid data and other large optical surveys.
We present the results of our first year of quasar search in the on-going ESO public Kilo Degree Survey (KiDS) and VISTA Kilo-Degree Infrared Galaxy (VIKING) surveys. These surveys are among the deeper wide-field surveys that can be used to uncovered large numbers of z~6 quasars. This allows us to probe a more common population of z~6 quasars that is fainter than the well-studied quasars from the main Sloan Digital Sky Survey. From this first set of combined survey catalogues covering ~250 deg^2 we selected point sources down to Z_AB=22 that had a very red i-Z (i-Z>2.2) colour. After follow-up imaging and spectroscopy, we discovered four new quasars in the redshift range 5.8<z<6.0. The absolute magnitudes at a rest-frame wavelength of 1450 A are between -26.6 < M_1450 < -24.4, confirming that we can find quasars fainter than M^*, which at z=6 has been estimated to be between M^*=-25.1 and M^*=-27.6. The discovery of 4 quasars in 250 deg^2 of survey data is consistent with predictions based on the z~6 quasar luminosity function. We discuss various ways to push the candidate selection to fainter magnitudes and we expect to find about 30 new quasars down to an absolute magnitude of M_1450=-24. Studying this homogeneously selected faint quasar population will be important to gain insight into the onset of the co-evolution of the black holes and their stellar hosts.
In this paper, we present the tools used to search for galaxy clusters in the Kilo Degree Survey (KiDS), and our first results. The cluster detection is based on an implementation of the optimal filtering technique that enables us to identify clusters as over-densities in the distribution of galaxies using their positions on the sky, magnitudes, and photometric redshifts. The contamination and completeness of the cluster catalog are derived using mock catalogs based on the data themselves. The optimal signal to noise threshold for the cluster detection is obtained by randomizing the galaxy positions and selecting the value that produces a contamination of less than 20%. Starting from a subset of clusters detected with high significance at low redshifts, we shift them to higher redshifts to estimate the completeness as a function of redshift: the average completeness is ~ 85%. An estimate of the mass of the clusters is derived using the richness as a proxy. We obtained 1858 candidate clusters with redshift 0 < z_c < 0.7 and mass 13.5 < log(M500/Msun) < 15 in an area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more than 50% of the clusters (77% in the case of the redMaPPer catalog). We also cross-matched our cluster catalog with the Abell clusters, and clusters found by XMM and in the Planck-SZ survey; however, only a small number of them lie inside the KiDS area currently available.
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