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Register a new userSpatial field reconstruction with INLA: Application to IFU galaxy data
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Added by
Emille E. O. Ishida
Publication date
2018
fields
Physics
and research's language is
English
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Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode auto-correlated spatial structures that cannot be optimally exploited by standard methodologies. This work introduces a novel technique to model IFS datasets, which treats the observed galaxy properties as realizations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference, the Integrated Nested Laplace Approximation (INLA). As a case study, we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO surveys, for which we retrieve the maps of the following physical properties: age, metallicity, mass and extinction. The proposed Bayesian approach, built on a generative representation of the galaxy properties, enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in datasets subject to substantial noise and/or sparsity of sampling. A snippet code to reproduce the analysis of this paper is available in the COIN toolbox, together with the field reconstructions of the CALIFA and PISCO samples.
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MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is an integral-field spectroscopic survey of 10,000 nearby galaxies that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). MaNGAs 17 pluggable optical fiber-bundle integral field units (IFUs) are deployed across a 3 deg field, they yield spectral coverage 3600-10,300 Ang at a typical resolution R ~ 2000, and sample the sky with 2 diameter fiber apertures with a total bundle fill factor of 56%. Observing over such a large field and range of wavelengths is particularly challenging for obtaining uniform and integral spatial coverage and resolution at all wavelengths and across each entire fiber array. Data quality is affected by the IFU construction technique, chromatic and field differential refraction, the adopted dithering strategy, and many other effects. We use numerical simulations to constrain the hardware design and observing strategy for the survey with the aim of ensuring consistent data quality that meets the survey science requirements while permitting maximum observational flexibility. We find that MaNGA science goals are best achieved with IFUs composed of a regular hexagonal grid of optical fibers with rms displacement of 5 microns or less from their nominal packing position, this goal is met by the MaNGA hardware, which achieves 3 microns rms fiber placement. We further show that MaNGA observations are best obtained in sets of three 15-minute exposures dithered along the vertices of a 1.44 arcsec equilateral triangle, these sets form the minimum observational unit, and are repeated as needed to achieve a combined signal-to-noise ratio of 5 per Angstrom per fiber in the r-band continuum at a surface brightness of 23 AB/arcsec^2. (abbrev.)
Reconstructing 3D distributions from their 2D projections is a ubiquitous problem in various scientific fields, particularly so in observational astronomy. In this work, we present a new approach to solving this problem: a Vienna inverse-Abel-transform based object reconstruction algorithm AVIATOR. The reconstruction that it performs is based on the assumption that the distribution along the line of sight is similar to the distribution in the plane of projection, which requires a morphological analysis of the structures in the projected image. The output of the AVIATOR algorithm is an estimate of the 3D distribution in the form of a reconstruction volume that is calculated without the problematic requirements that commonly occur in other reconstruction methods such as symmetry in the plane of projection or modelling of radial profiles. We demonstrate the robustness of the technique to different geometries, density profiles, and noise by applying the AVIATOR algorithm to several model objects. In addition, the algorithm is applied to real data: We reconstruct the density and temperature distributions of two dense molecular cloud cores and find that they are in excellent agreement with profiles reported in the literature. The AVIATOR algorithm is thus capable of reconstructing 3D distributions of physical quantities consistently using an intuitive set of assumptions.
Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is an optical fiber-bundle integral-field unit (IFU) spectroscopic survey that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). With a spectral coverage of 3622 - 10,354 Angstroms and an average footprint of ~ 500 arcsec^2 per IFU the scientific data products derived from MaNGA will permit exploration of the internal structure of a statistically large sample of 10,000 low redshift galaxies in unprecedented detail. Comprising 174 individually pluggable science and calibration IFUs with a near-constant data stream, MaNGA is expected to obtain ~ 100 million raw-frame spectra and ~ 10 million reduced galaxy spectra over the six-year lifetime of the survey. In this contribution, we describe the MaNGA Data Reduction Pipeline (DRP) algorithms and centralized metadata framework that produces sky-subtracted, spectrophotometrically calibrated spectra and rectified 3-D data cubes that combine individual dithered observations. For the 1390 galaxy data cubes released in Summer 2016 as part of SDSS-IV Data Release 13 (DR13), we demonstrate that the MaNGA data have nearly Poisson-limited sky subtraction shortward of ~ 8500 Angstroms and reach a typical 10-sigma limiting continuum surface brightness mu = 23.5 AB/arcsec^2 in a five arcsec diameter aperture in the g band. The wavelength calibration of the MaNGA data is accurate to 5 km/s rms, with a median spatial resolution of 2.54 arcsec FWHM (1.8 kpc at the median redshift of 0.037) and a median spectral resolution of sigma = 72 km/s.
High-precision astrometry requires accurate point-spread function modeling and accurate geometric-distortion corrections. This paper demonstrates that it is possible to achieve both requirements with data collected at the high acuity wide-field K-band imager (HAWK-I), a wide-field imager installed at the Nasmyth focus of UT4/VLT ESO 8m telescope. Our final astrometric precision reaches ~3 mas per coordinate for a well-exposed star in a single image with a systematic error less than 0.1 mas. We constructed calibrated astro-photometric catalogs and atlases of seven fields: the Baades Window, NGC 6656, NGC 6121, NGC 6822, NGC 6388, NGC 104, and the James Webb Space Telescope calibration field in the Large Magellanic Cloud. We make these catalogs and images electronically available to the community. Furthermore, as a demonstration of the efficacy of our approach, we combined archival material taken with the optical wide-field imager at the MPI/ESO 2.2m with HAWK-I observations. We showed that we are able to achieve an excellent separation between cluster members and field objects for NGC 6656 and NGC 6121 with a time base-line of about 8 years. Using both HST and HAWK-I data, we also study the radial distribution of the SGB populations in NGC 6656 and conclude that the radial trend is flat within our uncertainty. We also provide membership probabilities for most of the stars in NGC 6656 and NGC 6121 catalogs and estimate membership for the published variable stars in these two fields.
Integral field spectroscopy can map astronomical objects spatially and spectroscopically. Due to instrumental and atmospheric effects, it is common for integral field instruments to yield a sampling of the sky image that is both irregular and wavelength-dependent. Most subsequent analysis procedures require a regular, wavelength independent sampling (for example a fixed rectangular grid), and thus an initial step of fundamental importance is to resample the data onto a new grid. The best possible resampling would produce a well-sampled image, with a resolution equal to that imposed by the intrinsic spatial resolution of the instrument, telescope, and atmosphere, and with no statistical correlations between neighboring pixels. A standard method in the field to produce a regular set of samples from an irregular set of samples is Shepards method, but Shepards method typically yields images with a degraded resolution and large statistical correlations between pixels. Here we introduce a new method, which improves on Shepards method in both these respects. We apply this method to data from the Mapping Nearby Galaxies at Apache Point Observatory survey, part of Sloan Digital Sky Survey IV, demonstrating a full-width half maximum close to that of the intrinsic resolution (and ~ 16% better than Shepards method) and low statistical correlations between pixels. These results nearly achieve the ideal resampling. This method can have broader applications to other integral field data sets and to other astronomical data sets (such as dithered images) with irregular sampling.
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