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Register a new userExtinction Corrected Star Formation Rates Empirically Derived from Ultraviolet-Optical Colors
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Using a sample of galaxies from the Sloan Digital Sky Survey spectroscopic catalog with measured star-formation rates (SFRs) and ultraviolet (UV) photometry from the GALEX Medium Imaging Survey, we derived empirical linear correlations between the SFR to UV luminosity ratio and the UV-optical colors of blue sequence galaxies. The relations provide a simple prescription to correct UV data for dust attenuation that best reconciles the SFRs derived from UV and emission line data. The method breaks down for the red sequence population as well as for very blue galaxies such as the local ``supercompact UV luminous galaxies and the majority of high redshift Lyman Break Galaxies which form a low attenuation sequence of their own.
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In this paper we present the most up-to-date list of nearby galaxies with optically detected supernova remnants (SNRs). We discuss the contribution of the H{alpha} flux from the SNRs to the total H{alpha} flux and its influence on derived star formation rate (SFR) for 18 galaxies in our sample. We found that the contribution of SNRs flux to the total H{alpha} flux is 5 $pm$ 5 per cent. Due to the observational selection effects, the SNRs contamination of SFRs derived herein represents only a lower limit.
Using a local reference sample of 21 galaxies, we compare observations of the $lambda$2.16 $mu$m Brackett-$gamma$ (Br$gamma$) hydrogen recombination line with predictions from the Prospector Bayesian inference framework, which was used to fit the broadband photometry of these systems. This is a clean test of the spectral-energy-distribution-derived star formation rates (SFRs), as dust is expected to be optically thin at this wavelength in nearly all galaxies; thus, the internal conversion of SFR to predicted line luminosity does not depend strongly on the adopted dust model and posterior dust parameters, as is the case for shorter-wavelength lines such as H$alpha$. We find that Prospector predicts Br$gamma$ luminosities and equivalent widths with small offsets ($sim$0.05 dex), and scatter ($sim$0.2 dex), consistent with measurement uncertainties, though we caution that the derived offset is dependent on the choice of stellar isochrones. We demonstrate that even when the Prospector-derived dust attenuation does not well describe, e.g., H$alpha$ line properties or observed reddening between H$alpha$ and Br$gamma$, the underlying SFRs are accurate, as verified by the dust-free Br$gamma$ comparison. Finally, we discuss in what ways Br$gamma$ might be able to help constrain model parameters when treated as an input to the model, and comment on its potential as an accurate monochromatic SFR indicator in the era of JWST multiobject near-IR spectroscopy.
We present results of a detailed study aiming at understanding to what precision star formation histories (SFHs) can be determined for distant galaxies observable in integrated light only. Using our evolutionary synthesis code, we have performed a set of simulations of galaxies with a wide range of different SFHs. By analysing the resulting colors, spectra and Lick indices, we investigate to which extent different SF scenarios can be discriminated on the basis of their photometric and spectral properties, respectively. We find the robust result that no later than 4 Gyrs after the latest episode of enhanced star formation all scenarios exhibit very similar colors and indices; in practice, it is not possible to distinguish different scenarios of star formation which have evolved for more than 1, at the utmost 3-4 Gyrs since the last star forming event, even when using spectral indices. For how long different SF scenarios can be disentangled highly depends on the range of colors available and absorption lines considered, as well as on the details of the SFHs to be compared.
Compact groups of galaxies provide insight into the role of low-mass, dense environments in galaxy evolution because the low velocity dispersions and close proximity of galaxy members result in frequent interactions that take place over extended timescales. We expand the census of star formation in compact group galaxies by citet{tzanavaris10} and collaborators with Swift UVOT, Spitzer IRAC and MIPS 24 micron photometry of a sample of 183 galaxies in 46 compact groups. After correcting luminosities for the contribution from old stellar populations, we estimate the dust-unobscured star formation rate (SFR$_{mathrm{UV}}$) using the UVOT uvw2photometry. Similarly, we use the MIPS 24 micron photometry to estimate the component of the SFR that is obscured by dust (SFR$_{mathrm{IR}}$). We find that galaxies which are MIR-active (MIR-red), also have bluer UV colours, higher specific star formation rates, and tend to lie in H~{sc i}-rich groups, while galaxies that are MIR-inactive (MIR-blue) have redder UV colours, lower specific star formation rates, and tend to lie in H~{sc i}-poor groups. We find the SFRs to be continuously distributed with a peak at about 1 M$_{odot}$ yr$^{-1}$, indicating this might be the most common value in compact groups. In contrast, the specific star formation rate distribution is bimodal, and there is a clear distinction between star-forming and quiescent galaxies. Overall, our results suggest that the specific star formation rate is the best tracer of gas depletion and galaxy evolution in compact groups.
Star-formation activity is a key property to probe the structure formation and hence characterise the large-scale structures of the universe. This information can be deduced from the star formation rate (SFR) and the stellar mass (Mstar), both of which, but especially the SFR, are very complex to estimate. Determining these quantities from UV, optical, or IR luminosities relies on complex modeling and on priors on galaxy types. We propose a method based on the machine-learning algorithm Random Forest to estimate the SFR and the Mstar of galaxies at redshifts in the range 0.01<z<0.3, independent of their type. The machine-learning algorithm takes as inputs the redshift, WISE luminosities, and WISE colours in near-IR, and is trained on spectra-extracted SFR and Mstar from the SDSS MPA-JHU DR8 catalogue as outputs. We show that our algorithm can accurately estimate SFR and Mstar with scatters of sigma_SFR=0.38 dex and sigma_Mstar=0.16 dex for SFR and stellar mass, respectively, and that it is unbiased with respect to redshift or galaxy type. The full-sky coverage of the WISE satellite allows us to characterise the star-formation activity of all galaxies outside the Galactic mask with spectroscopic redshifts in the range 0.01<z<0.3. The method can also be applied to photometric-redshift catalogues, with best scatters of sigma_SFR=0.42 dex and sigma_Mstar=0.24 dex obtained in the redshift range 0.1<z<0.3.
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