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Register a new userDetailed Structural Decomposition of Galaxy Images
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Chien Y. Peng
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We present a two-dimensional (2-D) fitting algorithm (GALFIT) designed to extract structural components from galaxy images, with emphasis on closely modeling light profiles of spatially well-resolved, nearby galaxies observed with the Hubble Space Telescope. Our algorithm improves on previous techniques in two areas, by being able to simultaneously fit a galaxy with an arbitrary number of components, and with optimization in computation speed, suited for working on large galaxy images. We use 2-D models such as the ``Nuker law, the Sersic (de Vaucouleurs) profile, an exponential disk, and Gaussian or Moffat functions. The azimuthal shapes are generalized ellipses that can fit disky and boxy components. Many galaxies with complex isophotes, ellipticity changes, and position-angle twists can be modeled accurately in 2-D. When examined in detail, we find that even simple-looking galaxies generally require at least three components to be modeled accurately, rather than the one or two components more often employed. We illustrate this by way of 7 case studies, which include regular and barred spiral galaxies, highly disky lenticular galaxies, and elliptical galaxies displaying various levels of complexities. A useful extension of this algorithm is to accurately extract nuclear point sources in galaxies. We compare 2-D and 1-D extraction techniques on simulated images of galaxies having nuclear slopes with different degrees of cuspiness, and we then illustrate the application of the program to several examples of nearby galaxies with weak nuclei.
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We present a two-dimensional (2-D) fitting algorithm (GALFIT, Version 3) with new capabilities to study the structural components of galaxies and other astronomical objects in digital images. Our technique improves on previous 2-D fitting algorithms by allowing for irregular, curved, logarithmic and power-law spirals, ring and truncated shapes in otherwise traditional parametric functions like the Sersic, Moffat, King, Ferrer, etc., profiles. One can mix and match these new shape features freely, with or without constraints, apply them to an arbitrary number of model components and of numerous profile types, so as to produce realistic-looking galaxy model images. Yet, despite the potential for extreme complexity, the meaning of the key parameters like the Sersic index, effective radius or luminosity remain intuitive and essentially unchanged. The new features have an interesting potential for use to quantify the degree of asymmetry of galaxies, to quantify low surface brightness tidal features beneath and beyond luminous galaxies, to allow more realistic decompositions of galaxy subcomponents in the presence of strong rings and spiral arms, and to enable ways to gauge the uncertainties when decomposing galaxy subcomponents. We illustrate these new features by way of several case studies that display various levels of complexity.
We present the results of two-component (disc+bar) and three-component (disc+bar+bulge) multiwavelength 2D photometric decompositions of barred galaxies in five SDSS bands ($ugriz$). This sample of $sim$3,500 nearby ($z<0.06$) galaxies with strong bars selected from the Galaxy Zoo citizen science project is the largest sample of barred galaxies to be studied using photometric decompositions which include a bar component. With detailed structural analysis we obtain physical quantities such as the bar- and bulge-to-total luminosity ratios, effective radii, Sersic indices and colours of the individual components. We observe a clear difference in the colours of the components, the discs being bluer than the bars and bulges. An overwhelming fraction of bulge components have Sersic indices consistent with being pseudobulges. By comparing the barred galaxies with a mass-matched and volume-limited sample of unbarred galaxies, we examine the connection between the presence of a large-scale galactic bar and the properties of discs and bulges. We find that the discs of unbarred galaxies are significantly bluer compared to the discs of barred galaxies, while there is no significant difference in the colours of the bulges. We find possible evidence of secular evolution via bars that leads to the build-up of pseudobulges and to the quenching of star formation in the discs. We identify a subsample of unbarred galaxies with an inner lens/oval and find that their properties are similar to barred galaxies, consistent with an evolutionary scenario in which bars dissolve into lenses. This scenario deserves further investigation through both theoretical and observational work.
We introduce PHI, a fully Bayesian Markov-chain Monte Carlo algorithm designed for the structural decomposition of galaxy images. PHI uses a triple layer approach to effectively and efficiently explore the complex parameter space. Combining this with the use of priors to prevent nonphysical models, PHI offers a number of significant advantages for estimating surface brightness profile parameters over traditional optimisation algorithms. We apply PHI to a sample of synthetic galaxies with SDSS-like image properties to investigate the effect of galaxy properties on our ability to recover unbiased and well constrained structural parameters. In two-component bulge+disc galaxies we find that the bulge structural parameters are recovered less well than those of the disc, particularly when the bulge contributes a lower fraction to the luminosity, or is barely resolved with respect to the pixel scale or PSF. There are few systematic biases, apart from for bulge+disc galaxies with large bulge Sersic parameter, n. On application to SDSS images, we find good agreement with other codes, when run on the same images with the same masks, weights, and PSF. Again, we find that bulge parameters are the most difficult to constrain robustly. Finally, we explore the use of a Bayesian Information Criterion (BIC) method for deciding whether a galaxy has one- or two-components.
Galaxies are complex systems made up of different structural components such as bulges, discs, and bars. Understanding galaxy evolution requires unveiling, independently, their history of stellar mass and metallicity assembly. We introduce C2D, a new algorithm to perform spectro-photometric multi-component decompositions of integral field spectroscopy (IFS) datacubes. The galaxy surface-brightness distribution at each wavelength (quasi-monochromatic image) is fitted using GASP2D, a 2D photometric decomposition code. As a result, C2D provides both a characteristic one-dimensional spectra and a full datacube with all the spatial and spectral information for every component included in the fit. We show the basic steps of the C2D spectro-photometric fitting procedure, tests on mock datacubes demonstrating its reliability, and a first application of C2D to a sample of three early-type galaxies (ETGs) observed within the CALIFA survey. The resulting datacubes from C2D are processed through the PIPE3D pipeline obtaining both the stellar populations and ionised gas properties of bulges and discs. This paper presents an overview of the potential of C2D+PIPE3D to unveil the formation and evolution of galaxies.
The robust coding of natural images and the effective compression of encrypted images have been studied individually in recent years. However, little work has been done in the robust coding of encrypted images. The existing results in these two individual research areas cannot be combined directly for the robust coding of encrypted images. This is because the robust coding of natural images relies on the elimination of spatial correlations using sparse transforms such as discrete wavelet transform (DWT), which is ineffective to encrypted images due to the weak correlation between encrypted pixels. Moreover, the compression of encrypted images always generates code streams with different significance. If one or more such streams are lost, the quality of the reconstructed images may drop substantially or decoding error may exist, which violates the goal of robust coding of encrypted images. In this work, we intend to design a robust coder, based on compressive sensing with structurally random matrix, for encrypted images over packet transmission networks. The proposed coder can be applied in the scenario that Alice needs a semi-trusted channel provider Charlie to encode and transmit the encrypted image to Bob. In particular, Alice first encrypts an image using globally random permutation and then sends the encrypted image to Charlie who samples the encrypted image using a structural matrix. Through an imperfect channel with packet loss, Bob receives the compressive measurements and reconstructs the original image by joint decryption and decoding. Experimental results show that the proposed coder can be considered as an efficient multiple description coder with a number of descriptions against packet loss.
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