Describing 3 D Datasets Igor Chilingarian CRAL Observatoire

Describing 3 D Datasets Igor Chilingarian (CRAL Observatoire de Lyon/Sternberg Astronomical Institute of the Moscow State University) IVOA DM & DAL WG – VO France Spec WG – Euro 3 D

What is 3 D spectroscopy? IFU Spectroscopy SCIENCE CASE 1: History of the stellar population in nearby galaxies Search for stellar population subcomponents and their connection with kinematical substructures by making analysis of spectra integrated along the line of sight (presentation by P. Prugniel in the Theory IG session) • Usage of high-resolution synthetic spectra • Precise description of the instrumental response (spectral resolution) of the spectrograph is essential: LSF(x, y, l) in case of 3 D SCIENCE CASE 2: Stellar populations in AGNs Making 3 D spatial-spectral decomposition of the active nucleus will allow to study stellar population and star formation in inner regions of active galaxies. In addition to the spectral resolution we need to know spatial PSF built by the telescope+spectrograph. IVOA Interop, Kyoto, 2005 May 19

What is 3 D spectroscopy? SCIENCE CASE 1: Studies of gas kinematics in disc galaxies Scanning Fabry-Perot Interferometer From 2 D velocity field it is possible to derive a rotation curve in the case of rotating disc galaxies by taking into account non-circular motions and perturbations caused by internal structures in the galaxies (spiral arms, bars) and then to study the distribution of dark and luminous matter. By comparing these results to the simulations one is able to understand the evolution of the galaxies’ dynamics in a given environment (credits: O. Garrido) SCIENCE CASE 2: Star-formation complexes and supernovae remnants. Data in nearby galaxies: shells of ionized gas Processing Studies of supernovae remnants and expanding shells around starforming regions (stellar winds) in the nearby galaxies (e. g. dwarf irregulars) allows to study gas-removal mechanism and its rate, chemical composition variations and ISM distribution, that might shed light on the evolution of dwarf irregular galaxies. To study shell-like structures, one creates so-called positionvelocity diagrams: Phase Surfaceplanes slicing the IFP data cube (credits: T. Lozinskaya). IVOA Interop, Kyoto, 2005 May 19

Storing 3 D Data in FITS Key point: no information should be lost one should avoid resampling n Pure 3 D data cube (for IFP data and for some IFU) n 2 D-image (one spectrum per row) + binary table Euro 3 D Format • FITS binary data table: one row per spectrum • Binary table describing shape of spatial elements (“spaxels”) • Some mandatory metadata, including: common spectral WCS for all spectra, common spatial WCS for all spatial elements, meteo parameters during the observations, etc. IVOA Interop, Kyoto, 2005 May 19

What we propose Simple demo at the end of the year: n VO access to two science-ready data archives: ESO (SINFONI) and SAO RAS (MPFS) n Retrieving the data n Visualising them using Euro 3 D Visualisation Tool What we need: n n n DAL protocol: will SSA 1 be sufficient? Some modifications of the Euro 3 D Visualisation Tool Some “upgrade” of the Euro 3 D format (optional) IVOA Interop, Kyoto, 2005 May 19

Describing 3 D dataset Important 1. Atmosphere dispersion correction (if not made) information about meteo conditions is required characterisation is not sufficient: use observation DM 2. Spectral and spatial resolutions (LSF and PSF) depend both on spatial and spectral coordinates: LSF(x, y, l), PSF(x, y, l) 3. Principal question: How to characterise image slicing? IVOA Interop, Kyoto, 2005 May 19