Query Driven Visualization

The request driven way of deriving data in Astro-WISE is extended to a query driven way of visualization. This allows scientists to focus on the science they want to perform, because all administration of their data is automated. This can be done over an abstraction layer that enhances control and flexibility for the scientist.Comment: 4 pages, Procedings ADASS XXI, ASP Conference Serie

Astro-WISE processing of wide-field images and other data

Astro-WISE is the Astronomical Wide-field Imaging System for Europe. It is a scientific information system which consists of hardware and software federated over about a dozen institutes throughout Europe. It has been developed to exploit the ever increasing avalanche of data produced by astronomical surveys and data intensive scientific experiments in general. The demo explains the architecture of the Astro-WISE information system and shows the use of Astro-WISE interfaces. Wide-field astronomical images are derived from the raw image to the final catalog according to the user's request. The demo is based on the standard Astro-WISE guided tour, which can be accessed from the Astro-WISE website. The typical Astro-WISE data processing chain is shown, which can be used for data handling for a variety of different instruments, currently 14, including OmegaCAM, MegaCam, WFI, WFC, ACS/HST, etc.Comment: 4 pages, Procedings of ADASS XXI, ASP Conference Serie

Finding and Visualizing Relevant Subspaces for Clustering High-Dimensional Astronomical Data Using Connected Morphological Operators

Data sets in many scientific areas are growing to enormous sizes. For example, modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Gene expression ex-periments produce data about the complete genome of an organism under different conditions and at a sequence of time points. Ex-ploration of such very high-dimensional data spaces poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent years. How-ever, many clustering algorithms require the user to set a large num-ber of parameters without any guidelines. Some methods also do not provide a concise summary of the datasets, or, if they do, they lack additional important information such as the number of clus-ters present or the significance of the clusters

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure

First test of Verlinde's theory of Emergent Gravity using weak gravitational lensing measurements

Verlinde (2016) proposed that the observed excess gravity in galaxies and clusters is the consequence of Emergent Gravity (EG). In this theory the standard gravitational laws are modified on galactic and larger scales due to the displacement of dark energy by baryonic matter. EG gives an estimate of the excess gravity (described as an apparent dark matter density) in terms of the baryonic mass distribution and the Hubble parameter. In this work we present the first test of EG using weak gravitational lensing, within the regime of validity of the current model. Although there is no direct description of lensing and cosmology in EG yet, we can make a reasonable estimate of the expected lensing signal of low redshift galaxies by assuming a background LambdaCDM cosmology. We measure the (apparent) average surface mass density profiles of 33,613 isolated central galaxies, and compare them to those predicted by EG based on the galaxies' baryonic masses. To this end we employ the ~180 square degrees overlap of the Kilo-Degree Survey (KiDS) with the spectroscopic Galaxy And Mass Assembly (GAMA) survey. We find that the prediction from EG, despite requiring no free parameters, is in good agreement with the observed galaxy-galaxy lensing profiles in four different stellar mass bins. Although this performance is remarkable, this study is only a first step. Further advancements on both the theoretical framework and observational tests of EG are needed before it can be considered a fully developed and solidly tested theory

The third data release of the Kilo-Degree Survey and associated data products

The Kilo-Degree Survey (KiDS) is an ongoing optical wide-field imaging survey with the OmegaCAM camera at the VLT Survey Telescope. It aims to image 1500 square degrees in four filters (ugri). The core science driver is mapping the large-scale matter distribution in the Universe, using weak lensing shear and photometric redshift measurements. Further science cases include galaxy evolution, Milky Way structure, detection of high-redshift clusters, and finding rare sources such as strong lenses and quasars. Here we present the third public data release (DR3) and several associated data products, adding further area, homogenized photometric calibration, photometric redshifts and weak lensing shear measurements to the first two releases. A dedicated pipeline embedded in the Astro-WISE information system is used for the production of the main release. Modifications with respect to earlier releases are described in detail. Photometric redshifts have been derived using both Bayesian template fitting, and machine-learning techniques. For the weak lensing measurements, optimized procedures based on the THELI data reduction and lensfit shear measurement packages are used. In DR3 stacked ugri images, weight maps, masks, and source lists for 292 new survey tiles (~300 sq.deg) are made available. The multi-band catalogue, including homogenized photometry and photometric redshifts, covers the combined DR1, DR2 and DR3 footprint of 440 survey tiles (447 sq.deg). Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5 sigma in a 2 arcsec aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7 arcsec. The photometric homogenization scheme ensures accurate colors and an absolute calibration stable to ~2% for gri and ~3% in u. Separately released are a weak lensing shear catalogue and photometric redshifts based on two different machine-learning techniques.Comment: small modifications; 27 pages, 12 figures, accepted for publication in Astronomy & Astrophysic