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Register a new userIntroducing piXedfit -- a Spectral Energy Distribution Fitting Code Designed for Resolved Sources
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Added by
Abdurro'uf Abdurro'uf
Publication date
2021
fields
Physics
and research's language is
English
Authors
Abdurrouf
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We present piXedfit, pixelized spectral energy distribution (SED) fitting, a Python package that provides tools for analyzing spatially resolved properties of galaxies using multiband imaging data alone or in combination with integral field spectroscopy (IFS) data. piXedfit has six modules that can handle all tasks in the spatially resolved SED fitting. The SED fitting module uses the Bayesian inference technique with two kinds of posteriors sampling methods: Markov Chain Monte Carlo (MCMC) and random densely-sampling of parameter space (RDSPS). We test the performance of the SED fitting module using mock SEDs of simulated galaxies from IllustrisTNG. The SED fitting with both posteriors sampling methods can recover physical properties and star formation histories of the IllustrisTNG galaxies well. We further test the performance of piXedfit modules by analyzing 20 galaxies observed by the CALIFA and MaNGA surveys. The data comprises of 12-band imaging data from GALEX, SDSS, 2MASS, and WISE, and the IFS data from CALIFA or MaNGA. piXedfit can spatially match (in resolution and sampling) of the imaging and IFS data. By fitting only the photometric SEDs, piXedfit can predict the spectral continuum, $text{D}_{rm n}4000$, $H_{alpha}$, and $H_{beta}$ well. The star formation rate (SFR) derived by piXedfit is consistent with that derived from $H_{alpha}$ emission. The RDSPS method gives equally good fitting results as the MCMC and it is much faster than the MCMC. piXedfit is a versatile tool equipped with a parallel computing module for efficient analysis of large datasets, and will be made publicly available (https://github.com/aabdurrouf/piXedfit).
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We present MCSED, a new spectral energy distribution (SED)-fitting code, which mates flexible stellar evolution calculations with the Markov Chain Monte Carlo algorithms of the software package emcee. MCSED takes broad, intermediate, and narrow-band photometry, emission-line fluxes, and/or absorption line spectral indices, and returns probability distributions and co-variance plots for all model parameters. MCSED includes a variety of dust attenuation curves with parameters for varying the UV slopes and bump strengths, a prescription for continuum and PAH emission from dust, models for continuum and line emission from ionized gas, options for fixed and variable stellar metallicity, and a selection of star formation rate (SFR) histories. The code is well-suited for exploring parameter inter-dependencies in sets of galaxies with known redshifts, for which there is multi-band photometry and/or spectroscopy. We apply MCSED to a sample of $sim2000$ $1.90<z<2.35$ galaxies in the five CANDELS fields, which were selected via their strong [O III] $lambda5007$ emission, and explore the systematic behavior of their SEDs. We find the galaxies become redder with stellar mass, due to both increasing internal attenuation and a greater population of older stars. The slope of the UV extinction curve also changes with stellar mass, and at least some galaxies exhibit an extinction excess at 2175 Angstroms. Finally, we demonstrate that below $Mlesssim10^9,M_{odot}$), the shape of the star-forming galaxy main sequence is highly dependent on the galaxies assumed SFR history, as calculations which assume a constant SFR produce stellar masses that are $sim1$ dex smaller than those found using more realistic SFR histories.
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The physical parameters of galaxies and/or AGNs can be derived by fitting their multi-band spectral energy distributions (SEDs). By using CIGALE code, we perform multi-band SED fitting (from ultraviolet to infrared) for 791 X-ray sources (518 AGNs and 273 normal galaxies) in the 7 Ms Chandra Deep Field-south survey (CDFS). We consider the contributions from AGNs and adopt more accurate redshifts than published before. Therefore, more accurate star formation rates (SFRs) and stellar masses (M$_*$) are derived. We classify the 518 AGNs into type-I and type-II based on their optical spectra and their SEDs. Moreover, six AGN candidates are selected from the 273 normal galaxies based on their SEDs. Our main results are as follows: (1) the host galaxies of AGNs have larger M$_*$ than normal galaxies, implying that AGNs prefer to host in massive galaxies; (2) the specific star formation rates (sSFRs) of AGN host galaxies are different from those of normal galaxies, suggesting that AGN feedback may play an important role in the star formation activity; (3) we find that the fraction of optically obscured AGNs in CDFS decreases with the increase of intrinsic X-ray luminosity, which is consistent with previous studies;(4) the host galaxies of type-I AGNs tend to have lower M$_*$ than type-II AGNs, which may suggest that dust in the host galaxy may also contribute to the optical obscuration of AGNs.
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The spectral energy distribution of galaxies is a complex function of the star formation history and geometrical arrangement of stars and gas in galaxies. The computation of the radiative transfer of stellar radiation through the dust distribution is time-consuming. This aspect becomes unacceptable in particular when dealing with the predictions by semi-analytical galaxy formation models populating cosmological volumes, to be then compared with multi-wavelength surveys. Mainly for this aim, we have implemented an artificial neural network algorithm into the spectro-photometric and radiative transfer code GRASIL in order to compute the spectral energy distribution of galaxies in a short computing time. This allows to avoid the adoption of empirical templates that may have nothing to do with the mock galaxies output by models. The ANN has been implemented to compute the dust emission spectrum (the bottleneck of the computation), and separately for the star-forming molecular clouds and the diffuse dust (due to their different properties and dependencies). We have defined the input neurons effectively determining their emission, which means this implementation has a general applicability and is not linked to a particular galaxy formation model. We have trained the net for the disc and spherical geometries, and tested its performance to reproduce the SED of disc and starburst galaxies, as well as for a semi-analytical model for spheroidal galaxies. We have checked that for this model both the SEDs and the galaxy counts in the Herschel bands obtained with the ANN approximation are almost superimposed to the same quantities obtained with the full GRASIL. We conclude that this method appears robust and advantageous, and will present the application to a more complex SAM in another paper.
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