How VO gets richer and helps solving fundamental problems of extragalactic astrophysics
- Nov 26, 2012
- Observatoire de Paris
- Pierre Le Sidaner
Tutorial organized by VO-Paris Data Centre, build and framed by Ivan Zolotukhin (IRAP) and Igor Chilingarian (elescope Data Center, Smithsonian Astrophysical Observatory).
This VO-Science tutorial may be interesting for those who are willing to learn cross-domain multi-discipline way of dealing with rich astronomical data through mature technological stack provided by the Virtual Observatory for the ultimate benefit of the scientific problems. General notion of VO concepts and technologies is a plus.
Science case: Stellar populations and evolution of galaxies at low and intermediate redshifts. We will use the FUV-to-NIR SED catalogue of low and intermediate-redshift galaxies built from SDSS, UKIDSS, and GALEX data (its construction was showcased in the ADASS XX tutorial) with resources available in data archives and VO in order to study the galaxy evolution in different environments (groups, clusters, field) and see how different galaxy properties vary across the cosmic web using three-dimensional visualisation of the large scale structure of the Universe. We will use recently developed IVOA standards (TAP, ObsCore, SIAPv2) to access large archives and automatically collect data of our interest. New science results will include: (a) a population of compact elliptical galaxies from their multi-colour properties; (b) the mass--metallicity relation of galaxies as a function of the environment richness/density; (c) gas versus stellar metallicity for starforming galaxies; (d) connection between stellar mass and X-ray luminosity in galaxy clusters using publicly available X-ray data.
PC or Mac with SUN/Oracle Java Runtime Environment v.5+ and Internet connection; standard GNU command-line tools (wget, grep etc.) available in Linux/UNIX, MacOSX or Windows+Cygwin. Other required tools will be installed automatically using Java WebStart. Step-by-step instructions including command-line scripts to automate cross-match and access image cut-outs will be provided in a printed form and online.
- Software installation/configuration (10-15min)
- General introduction (10-15min)
- Collecting and cross-matching datasets, exploring the data (60 min)
- Break (5-10min)
- Obtaining scientific results and explaining them (60 min)
- Discussion and questions (15-20min)