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During this meeting we have discussed a lot about different methods of deriving star formation rates in galaxies and what the advantages and disadvantages of using different calibrations are (see the comprehensive review talk by Veronique Buat at this meeting; see also review by Calzetti 2012). During the session on SFR determinations from SED modelling, but also throughout the meeting, two SED modelling approaches have been presented and discussed: the energy balance method and the radiative transfer modelling.

The energy balance method relies on the conservation of energy between the stellar light aborbed by dust and that emitted (by the same dust) in the mid-IR/far-IR/submm. This is by far a superior method to only fitting SED templates in a limited spectral range (see talk by Denis Burgarella). Nonetheless, the energy-balance method is not a self-consistent analysis, since the SED of dust emission is not calculated according to the radiation fields originating from the stellar populations in the galaxy under study, but rather according to some templates. The templates can be either empirical (Xu et al. 1998, Devriendt et al. 1999, Sajina et al. 2006, Marshall et al. 2007) or theoretical (Dale & Helou 2002, Draine & Li 2007, Natale et al. 2010). At this meeting we saw applications of two energy balance methods, MAGPHYS (da Cuhna et al. 2008) and CIGALE (Burgarella et al. 2005, Noll et al. 2009). The energy balance method is a useful tool when dealing with large statistical samples of galaxies for which little information is available regarding morphology/type, orientation and overall size. This advantage comes nevertheless with the disadvantage that the energy balance methods cannot take into account the effect on the dust attenuation and therefore also on the dust emission of the different geometries of stars and dust present in galaxies of different morphological types, neither can it take into account the anisotropies in the predicted stellar light due to disk inclination (when a disk geometry is present). These methods can therefore only be used in a statistical sense, when dealing with overall trends in galaxy populations.

The radiative transfer method is the only one that can self-consistently calculate the dust emission SEDs based on an explicit calculation of the radiation fields heating the dust, consequently derived from the attenuated stellar populations in the galaxy under study. At this meeting we saw applications of the RT model of Popescu et al. (2011). This method can take advantage of the constraints provided by available optical information like morphology, disk-to-bulge ratio, disk inclination (when a disk morphology is present) and size. As opposed to the energy balance method, this advantage comes with the disatvantage that such information is not always easily available. Nonetheless these radiation transfer methofs could perhaps be adapted to incorporate this information (when missing) in the form of free parameters of the model, though such attempts have not yet been made. Another drawback of these methods was that radiative transfer calculations are notorious for being computationally very time consuming, and as such, detailed calculations have been mainly used for a small number of galaxies (Popescu et al. 2000, Misiriotis et al. 2001, Popescu et al. 2004, Bianchi 2008, Baes et al. 2010, MacLachlan et al. 2011, Schechtman-Rook et al. 2012, de Looze et al. 2012a, b). This situation has been recently changed, with the creation of large libraries of radiative transfer model SEDs, as performed by Siebenmorgen & Krugel (2007) for starburst galaxies, Groves et al. (2008) for star-forming regions/starburt galaxies and Popescu et al. (2011) for spiral galaxies.

Reviews on determination of star-formation in galaxies have so far not included discussions on the use of radiative transfer methods, with the exception of the review of Kylafis & Misiriotis (2006) With the new developments resulting in the creation of libraries of RT models, we can now start to include radiative models as main topics of discussion. Indeed, at this meeting we emphasised that they are in fact the most realible way of deriving star formation rates in galaxies. In this way the SFRs are derived self-consistently using information from the whole range of the electromagnetic spectrum, from the UV to the FIR/submm, incorporating information with morphological constraints (primarily from optical imaging). Here we did not consider radio and Xray emission, though these emissions have been discussed in other sessions of this meeting (e.g. talk by Bret Lehmer). In particular the SED modelling has been discussed in conjunction with the most difficult cases, namely those of translucent galaxies: galaxies with both optically thin and thick components. In one way optically thin galaxies are more easily dealt with, since most of the information on SFR can be derived from the UV. Very optically thick cases are also easy from this point of view, since SFR can be derived from their FIR emission, providing one can isolate the AGN powered emission. But the most difficult cases are the translucent galaxies. These are essentially the spiral galaxies in the Local Universe, and probably a large fraction of the star forming dwarf galaxies - which dominate the population of galaxies in the Local Universe, and which also host most of the star formation activity taking place in the local Universe. We showed in this meeting how important it is to quantify this star formation activity. We also discussed how important it is to quantify the star formation activity in the high redshift Universe; it is just that for the moment we do not have enough detailed information to be able to do the same type of analysis that we can now do for the Local Universe. Here I will summarise the main points we addressed:

1. Why is it so difficult to calibrate SFR in spiral galaxies and why do we need to follow the fate of photons with radiative transfer calculations?

2. Why do SFR calibrators work?

SED modelling tools based on self-consistent radiative transfer calculations can be used to predict the scatter in the SFR calibration relations, as a function of the main intrinsic parameters that can affect these relations. Several of these relations have been presented. In Fig. 4 we only show predictions for the SFR calibration based on monochromatic FIR luminosities when the dust opacity changes. The predictions are based on the model of Popescu et al. (2011). The figure shows a very large scatter in the correlations. A similar large scatter is predicted for correlations corresponding to various contributions coming from the old stellar populations, or for the clumpiness of the ISM. For the UV calibrators large scatters are also predicted when some of the relevant parameters (viewing angle, dust opacity, bulge-to-disk ratio and clumpiness of the ISM) vary. Overall it is apparent from these plots that the predicted scatter in the SFR correlations due to a broad range in parameter values is larger than observed in reality. The question then arises of why are the SFR calibrators working, despite, for example, the very crude dust corrections that have been so far used in the community? A possible answer is the existence of some scaling parameters, which do not allow a continuous variation in parameter space, in particular for dust opacity or stellar luminosity. Recent work from Grootes et al. (2013) proved the existence of a well-defined correlation between dust opacity and stellar mass density. The correlation was derived on data coming from Galaxy and Mass Assembly (GAMA) survey (Driver et al. 2011) and the Herschel ATLAS survey (Eales et al. 2011), in combination with the model of Popescu et al. (2011). These finding give support to the interpretation of the existence of fundamental physical relations that reduce the scatter in the SFR correlations.

Figure 4a Figure 4b

Figure 4. Predictions for the relation betwween the 70 µm (left) and 160 µm luminosity versus SFR based on the model of Popescu et al. (2011). From bottom to top the curves correspond to central face-on B band optical depth values of 0.1, 0.3, 0.5, 1.0, 2.0, 4.0 and 8.0.

3. A word of caution

We have identified some points where things should be treated more carefully in the future:

4. What else have we learned?

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