Abstract
While most astronomers are now familiar with tools to decompose images into multiple components such as disks, bulges, and halos, the equivalent techniques for spectral data cubes are still in their infancy. This is unfortunate, as integral field unit (IFU) spectral surveys are now producing a mass of data in this format, which we are ill-prepared to analyze effectively. We have therefore been developing new tools to separate out components using this full spectral data. The results of such analyses will prove invaluable in determining not only whether such decompositions have an astrophysical significance, but, where they do, also in determining the relationship between the various elements of a galaxy. Application to a pilot study of IFU data from the cD galaxy NGC 3311 confirms that the technique can separate the stellar halo from the underlying galaxy in such systems, and indicates that, in this case, the halo is older and more metal poor than the galaxy, consistent with it forming from the cannibalism of smaller satellite galaxies. The success of the method bodes well for its application to studying the larger samples of cD galaxies that IFU surveys are currently producing.
Highlights
Our understanding of galactic structure and its evolution has been driven forward on a tidal wave of data, which has allowed us to systematically quantify the properties of these beautiful systems on the basis of very large surveys
Multi-Unit Spectroscopic Explorer (MUSE) pointings, the image data in this single wavelength slice, the elliptical and halo components fitted as a de Vaucouleurs law and an exponential respectively, and the residuals once they are subtracted
We have shown how spectral data cubes can be analyzed in a manner analogous
Summary
Our understanding of galactic structure and its evolution has been driven forward on a tidal wave of data, which has allowed us to systematically quantify the properties of these beautiful systems on the basis of very large surveys. The Sloan Digital Sky Survey (SDSS) produced imaging data on millions of galaxies, which, though an amazing resource, presented the additional challenge of how to quantify so much information in a meaningful form Tools such as GALFIT had been developed that allow the properties of the components that make up galaxies to be objectively quantified by simultaneously fitting a number of simple functions such as two-dimensional Sersic profiles to their images to quantify each component [1]. Such automated techniques can readily be applied even to surveys on the scale of SDSS, producing a mass of summarizing data on the disks, bulges, and other components that make up galaxies in the nearby Universe [2].
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