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Register a new userReconstructing Galaxy Spectral Energy Distributions from Broadband Photometry
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I. Csabai
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We present a novel approach to photometric redshifts, one that merges the advantages of both the template fitting and empirical fitting algorithms, without any of their disadvantages. This technique derives a set of templates, describing the spectral energy distributions of galaxies, from a catalog with both multicolor photometry and spectroscopic redshifts. The algorithm is essentially using the shapes of the templates as the fitting parameters. From simulated multicolor data we show that for a small training set of galaxies we can reconstruct robustly the underlying spectral energy distributions even in the presence of substantial errors in the photometric observations. We apply these techniques to the multicolor and spectroscopic observations of the Hubble Deep Field building a set of template spectra that reproduced the observed galaxy colors to better than 10%. Finally we demonstrate that these improved spectral energy distributions lead to a photometric-redshift relation for the Hubble Deep Field that is more accurate than standard template-based approaches.
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The All-wavelength Extended Groth Strip International Survey (AEGIS) team presents broad-band spectral energy distributions (SEDs), from X-ray to radio wavelengths, for 71 galaxies spanning the redshift range 0.55-1.16 (<z>~0.7). Galaxies with secure redshifts were selected from a small (22 arcminute-square) sub-section of the Keck/DEIMOS galaxy redshift survey in the Extended Groth Strip field that has also been targeted for deep panchromatic imaging by Chandra (X-ray), GALEX (ultraviolet), Canada-France-Hawaii Telescope (optical), Hubble Space Telescope (optical/near infrared), Palomar Observatory (near infrared), Spitzer (mid/far infrared), and the Very Large Array (radio.) The absolute magnitude of the typical galaxy in our sample is M_B=-19.82. The ultraviolet to mid-infrared portion of their spectral energy distributions (SEDs) are found to be bracketed by two stellar-only model SEDs: an early burst followed by passive evolution and a constant star-formation rate since early times; this suggests that few of these galaxies are undergoing major starbursts. Approximately half the galaxies show a mid- to far-infrared excess relative to the model SEDs, consistent with thermal emission from interstellar dust. Two objects have power-law SEDs, indicating that they are dominated by active galactic nuclei; both are detected in X-rays. The galaxies are grouped by rest-frame color,quantitative optical morphology, and [OII] emission line strength (possible indicator of star formation). On average, the panchromatic SEDs of the galaxies, from the ultraviolet to the infrared, follow expected trends: redder SEDs are associated with red U-B, early-type morphology, and low [OII] emission, and vice versa for blue SEDs.
We address the problem of reconstructing a set of points on a line or a loop from their unassigned noisy pairwise distances. When the points lie on a line, the problem is known as the turnpike; when they are on a loop, it is known as the beltway. We approximate the problem by discretizing the domain and representing the $N$ points via an $N$-hot encoding, which is a density supported on the discretized domain. We show how the distance distribution is then simply a collection of quadratic functionals of this density and propose to recover the point locations so that the estimated distance distribution matches the measured distance distribution. This can be cast as a constrained nonconvex optimization problem which we solve using projected gradient descent with a suitable spectral initializer. We derive conditions under which the proposed distance distribution matching approach locally converges to a global optimizer at a linear rate. Compared to the conventional backtracking approach, our method jointly reconstructs all the point locations and is robust to noise in the measurements. We substantiate these claims with state-of-the-art performance across a number of numerical experiments. Our method is the first practical approach to solve the large-scale noisy beltway problem where the points lie on a loop.
(abridged) We present STARDUST, a new self-consistent modelling of the spectral energy distributions (SEDs) of galaxies from far-UV to radio wavelengths. In order to derive the SEDs in this broad spectral range, we first couple spectrophotometric and (closed-box) chemical evolutions to account for metallicity effects on the spectra of synthetic stellar populations. We then use a phenomenological fit for the metal-dependent extinction curve and a simple geometric distribution of the dust to compute the optical depth of galaxies and the corresponding obscuration curve. This enables us to calculate the fraction of stellar light reprocessed in the infrared range. In a final step, we define a dust model with various components and we fix the weights of these components in order to reproduce the IRAS correlation of IR colours with total IR luminosities. This allows us to compute far-IR SEDs that phenomenologically mimic observed trends. We are able to predict the spectral evolution of galaxies in a broad wavelength range, and we can reproduce the observed SEDs of local spirals, starbursts, luminous infrared galaxies (LIRGs) and ultra luminous infrared galaxies (ULIRGs). This modelling is so far kept as simple as possible and depends on a small number of free parameters, namely the initial mass function (IMF), star formation rate (SFR) time scale, gas density, and galaxy age, as well as on more refined assumptions on dust properties and the presence (or absence) of gas inflows/outflows.
We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.
Nearly a century after the true nature of galaxies as distant island universes was established, their origin and evolution remain great unsolved problems of modern astrophysics. One of the most promising ways to investigate galaxy formation is to study the ubiquitous globular star clusters that surround most galaxies. Recent advances in our understanding of the globular cluster systems of the Milky Way and other galaxies point to a complex picture of galaxy genesis driven by cannibalism, collisions, bursts of star formation and other tumultuous events.
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