Scientific Visualisation of Extremely Large Distributed Astronomical Surveys

Interactive real-time visualisation of large data sets plays an important role in scientific research. It is even more relevant for astronomy where new cutting edge large telescopes will generate tens of petabytes sky surveys. We describe our solution, developed in context of the Euclid space mission whose large astronomical imaging data will be distributed over several heterogeneous Science Data Centres (SDCs) across the world. In our visualisation architecture for distributed data, millions of survey images (HiPS) distributed over SDCs are efficiently transported and combined to deliver image(s) of interest at the desired resolution (up to pixel level) to the user. This is achieved by optimally utilising a combination of several modern tools consisting of http servers, a Front-End Node and load-balancer (FEN), reverse proxies, PHP/Python scripts, MySQL databases, including on the fly image generation/combination which all feed (only) the required information to the Aladin interactive visualisation tool at the remote user's Personal Computer (PC). It has potential applications for large projects (e.g., Square Kilometre Array) having data distributed across several locations

Scientific Visualisation of Extremely Large Distributed Astronomical Surveys

Interactive real-time visualisation of large data sets plays an important role in scientific research. It is even more relevant for astronomy where new cutting edge large telescopes will generate tens of petabytes sky surveys. We describe our solution, developed in context of the Euclid space mission whose large astronomical imaging data will be distributed over several heterogeneous Science Data Centres (SDCs) across the world. In our visualisation architecture for distributed data, millions of survey images (HiPS) distributed over SDCs are efficiently transported and combined to deliver image(s) of interest at the desired resolution (up to pixel level) to the user. This is achieved by optimally utilising a combination of several modern tools consisting of http servers, a Front-End Node and load-balancer (FEN), reverse proxies, PHP/Python scripts, MySQL databases, including on the fly image generation/combination which all feed (only) the required information to the Aladin interactive visualisation tool at the remote user's Personal Computer (PC). It has potential applications for large projects (e.g., Square Kilometre Array) having data distributed across several locations

KiDS-SQuaD: The KiDS Strongly lensed Quasar Detection project

New methods have been recently developed to search for strong gravitational lenses, in particular lensed quasars, in wide-field imaging surveys. Here, we compare the performance of three different, morphology- and photometry- based methods to find lens candidates over the Kilo-Degree Survey (KiDS) DR3 footprint (440 deg$^2$). The three methods are: i) a multiplet detection in KiDS-DR3 and/or Gaia-DR1, ii) direct modeling of KiDS cutouts and iii) positional offsets between different surveys (KiDS-vs-Gaia, Gaia-vs-2MASS), with purpose-built astrometric recalibrations. The first benchmark for the methods has been set by the recovery of known lenses. We are able to recover seven out of ten known lenses and pairs of quasars observed in the KiDS DR3 footprint, or eight out of ten with improved selection criteria and looser colour pre-selection. This success rate reflects the combination of all methods together, which, taken individually, performed significantly worse (four lenses each). One movelty of our analysis is that the comparison of the performances of the different methods has revealed the pros and cons of the approaches and, most of all, the complementarities. We finally provide a list of high-grade candidates found by one or more methods, awaiting spectroscopic follow-up for confirmation. Of these, KiDS 1042+0023 is to our knowledge the first confirmed lensed quasar from KiDS, exhibiting two quasar spectra at the same source redshift at either sides of a red galaxy, with uniform flux-ratio $f\approx1.25$ over the wavelength range $0.45\mu\mathrm{m}<\lambda<0.75\mu\mathrm{m}.$Comment: 12 pages, 4 figures, 4 tables, accepted for publication in MNRA

The galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3

We aim to investigate the galaxy environment in GAMA Galaxy Groups Catalogue (G3C) using a volume-limited galaxy sample from the Kilo Degree Survey Data Release 3. The k-Nearest Neighbour technique is adapted to take into account the probability density functions (PDFs) of photometric redshifts in our calculations. This algorithm was tested on simulated KiDS tiles, showing its capability of recovering the relation between galaxy colour, luminosity and local environment. The characterization of the galaxy environment in G3C groups shows systematically steeper density contrasts for more massive groups. The red galaxy fraction gradients in these groups is evident for most of group mass bins. The density contrast of red galaxies is systematically higher at group centers when compared to blue galaxy ones. In addition, distinct group center definitions are used to show that our results are insensitive to center definitions. These results confirm the galaxy evolution scenario which environmental mechanisms are responsible for a slow quenching process as galaxies fall into groups and clusters, resulting in a smooth observed colour gradients in galaxy systems.Comment: 14 pages, Accepted to MNRA

The first and second data releases of the Kilo-Degree Survey

Context. The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, its core science driver is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases include galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. Aims. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data. Public data releases, the first two of which are presented here, are crucial for enabling independent confirmation of the survey’s scientific value. The achieved data quality and initial scientific utilization are reviewed in order to validate the survey data. Methods. A dedicated pipeline and data management system based on ASTRO-WISE, combined with newly developed masking and source classification tools, is used for the production of the data products described here. Science projects based on these data products and preliminary results are outlined. Results. For 148 survey tiles (≈160 sq.deg.) stacked ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalogue. Limiting magnitudes are typically 24.3, 25.1, 24.9, 23.8 (5σ in a 2′′ aperture) in ugri, respectively, and the typical r-band PSF size is less than 0.7′′. The photometry prior to global homogenization is stable at the ~2% (4%) level in gri (u) with some outliers due to non-photometric conditions, while the astrometry shows a typical 2D rms of 0.03′′. Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and structural parameters for hundreds of thousands of galaxies

The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

The Astropy project supports and fosters the development of open-source and openly-developed Python packages that provide commonly-needed functionality to the astronomical community. A key element of the Astropy project is the core package Astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of inter-operable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy project