SEDeblend: a new method for deblending spectral energy distributions in confused imaging

Abstract

For high-redshift submillimetre or millimetre sources detected with single dish telescopes, interferometric follow-up has shown that many are multiple submm galaxies blended together. Confusion-limited Herschel observations of such targets are also available, and these sample the peak of their spectral energy distribution in the farinfrared. Many methods for analysing these data have been adopted, but most follow the traditional approach of extracting fluxes before model spectral energy distributions are fit, which has the potential to erase important information on degeneracies among fitting parameters and glosses over the intricacies of confusion noise. Here, we adapt the forward-modelling method that we originally developed to disentangle a high-redshift strongly-lensed galaxy group, in order to tackle this problem in a more statistically rigorous way, by combining source deblending and SED fitting into the same procedure. We call this method "SEDeblend." As an application, we derive constraints on farinfrared luminosities and dust temperatures for sources within the ALMA follow-up of the LABOCA Extended Chandra Deep Field South Submillimetre Survey. We find an average dust temperature for an 870 µm-selected sample of (33.9±2.4) K for the full survey. When selection effects of the sample are considered, we find no evidence that the average dust temperature evolves with redshift.

SCUBA-2 Ultra Deep Imaging EAO Survey (Studies). III. Multiwavelength Properties, Luminosity Functions, and Preliminary Source Catalog of 450 μm Selected Galaxies

“…On the other hand, our finding may be consistent with a recent study of high-resolution cosmological simulation where the mass-weighted dust temperature (based on radiative transfer modeling) does not strongly evolve with redshift over z = 2-6 (Liang et al 2019). We suggest that the evolution derived in previous studies might be biased by the T d selection effect (see also, Chapman et al 2004a;Chapin et al 2009;MacKenzie et al 2016) if they do not apply a similar L IR and z cuts as we do.…”

“…Our median of 38.3 +0.4 −0.9 K is between the estimates from previous studies of SCUBA-2 450-µm-selected samples ( T d = 42 ± 11 K, Roseboom et al 2013 ; Zavala et al 2018) and ALMA-identified LABOCA 870-µm-selected SMGs ( T d = 33 +3 −2 K, Simpson et al 2017). This may not be consistent with the expectation that a longer selection waveband tends to select cooler sources (see also Chapin et al 2009;MacKenzie et al 2016). This may be explained by the correlation between T d and L IR ( §5.2) and the fact that our observations are more sensitive to low-luminosity systems.…”

“Super-deblended” Dust Emission in Galaxies. I. The GOODS-North Catalog and the Cosmic Star Formation Rate Density out to Redshift 6

“…As an extension to XID + photometry, Pearson et al (2017) developed a method of incorporating galaxy SEDs as "informed priors," finding improvements in the detection of faint sources. More aggressively, there are also methods that fit multiband images simultaneously by fixing the shape of galaxy SEDs (e.g., MacKenzie et al 2016) in an approach inspired by Bayesian techniques (Budavári & Szalay 2008;e.g., their Equation (19)). The results from these approaches can be substantially affected by the assumptions about galaxy SED shapes.…”

“…More aggressively, there are also methods that fit multi-band images simultaneously by fixing the shape of galaxy SEDs (e.g., MacKenzie et al 2016) in an approach inspired by Bayesian techniques (Budavári & Szalay 2008, e.g., their Eq. 19).…”

Mr-Moose: an advanced SED-fitting tool for heterogeneous multi-wavelength data sets

“…It is important here to distinguish the two complementary aspects of SED fitting: the models used to predict the SED given a set of parameters and assumptions (Conroy 2013, for a recent review) and the algorithm used to estimate the parameters on data (the fitting itself, see Sharma 2017, for a review on the different available algorithms). During the last decade, these two aspects were often combined to provide users off-the-shelf SED fitting procedures with a varying wavelength range: Magphys (da Cunha et al 2008) for galaxy evolution in the UV-FIR range, BayesClumpy (Asensio Ramos & Ramos Almeida 2009) for AGN torus fitting in the UV-FIR range, CIGALE (Noll et al 2009) for galaxy evolution with AGN implementation in the UV-FIR range, BRATS (Harwood et al 2013) specialised for multi-radio frequency fitting in resolved sources, SEDfit (Sawicki 2012) for galaxy evolution in optical/NIR implementing spatially resolved sources, SEDeblend (MacKenzie et al 2016) for SED fitting in the context of lensed source with FIR observations, AGNFitter (Calistro Rivera et al 2016) focusing on the AGN component in the UV-FIR range, ... to name the most widely used in extragalactic astronomy 1 .…”