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Panchromatic Spectral Energy Distributions of simulated galaxies: results at redshift z=0

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arxiv 1610.09843 v1 pith:DJHSTKQA submitted 2016-10-31 astro-ph.GA

Panchromatic Spectral Energy Distributions of simulated galaxies: results at redshift z=0

classification astro-ph.GA
keywords galaxiesmassstellardustsimulatedobservedpropertiesseds
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present predictions of Spectral Energy Distributions (SEDs), from the UV to the FIR, of simulated galaxies at $z=0$. These were obtained by post-processing the results of an N-body+hydro simulation of a small cosmological volume, that uses the Multi-Phase Particle Integrator (MUPPI) for star formation and stellar feedback, with the GRASIL-3D radiative transfer code, that includes reprocessing of UV light by dust. Physical properties of galaxies resemble observed ones, though with some tension at small and large stellar masses. Comparing predicted SEDs of simulated galaxies with different samples of local galaxies, we find that these resemble observed ones, when normalised at 3.6 $\mu$m. A comparison with the Herschel Reference Survey shows that, when binning galaxies in Star Formation Rate (SFR), average SEDs are reproduced to within a factor of $\sim2$ even in normalization, while binning in stellar mass highlights the same tension that is present in the stellar mass -- SFR plane. We use our sample to investigate the correlation of IR luminosity in Spitzer and Herschel bands with several galaxy properties. SFR is the quantity that best correlates with IR light up to $160\ \mu$m, while at longer wavelengths better correlations are found with molecular mass and, at $500\ \mu$m, with dust mass. However, using the position of the FIR peak as a proxy for cold dust temperature, we assess that heating of cold dust is mostly determined by SFR, with stellar mass giving only a minor contribution. We finally show how our sample of simulated galaxies can be used as a guide to understand the physical properties and selection biases of observed samples.

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