In the absence of dust, the spectral emission of a normal star-forming galaxy is dominated by stellar populations of different ages with superimposed nebular emission, mainly in the form of recombination lines as well as continuum. The interaction with dust has a dramatic effect, both dimming and reddening the emission from stars and ionized gas. This negatively impacts our ability to measure star formation as energetic photons produced by massive young stars are far more easily attenuated than longer wavelength photons, and even a small quantity of dust can lead to a significant attenuation in the ultraviolet (UV). In the case of particularly dust-rich galaxies, it can render their detection in the rest-frame UV especially difficult. However, as the FUV emission vanishes due to dust attenuation, this dust re-emits the absorbed energy in the mid-infrared (MIR) and far-infrared (FIR), which can in turn be exploited to trace star formation. Except for the most extreme cases (e.g., when the dust content is negligible or, conversely, when almost all of the UV photons are absorbed by dust), an attenuation correction must be carried out to retrieve the star formation rate (SFR). One of the most direct ways is to simply apply a hybrid SFR estimator combining the rest-frame UV with the IR (e.g., Hao et al. 2011; Boquien et al. 2016). The obvious downside is that this requires observations of the dust emission that are costly and difficult to obtain, and even more so at increasing redshifts, where they tend to be limited to vanishingly small samples. With the rest-frame UV emission being relatively easy to obtain from the ground from z ∼ 2 and beyond, techniques have been developed to relate the UV slope (β) to the UV attenuation (the IRX-β relation). While this approach initially appeared to work remarkably well in the case of starburst galaxies (Meurer et al. 1999), there is now ample evidence that there is no tight universal relation between the UV slope and the attenuation (e.g., Buat et al. 2005; Seibert et al. 2005; Howell et al. 2010; Casey et al. 2014). In fact, this relation relies on two strong underlying assumptions: the intrinsic UV slope of the stellar populations in the absence of dust and the exact shape of the attenuation curve. Numerous studies have analyzed their respective impact in an attempt to understand why and when such relations fail and build more reliable ones (e.g., Kong et al. 2004; Boquien et al. 2009, 2012; Popping et al. 2017, and many others). In particular, the recent study of Salim & Boquien (2019) found that the diversity of attenuation curves is a strong driver of the scatter around the IRX-β relation. This finding, which is consistent with simulations (Narayanan et al. 2018b; Liang et al. 2021), is especially important in that we can observe a broad variety of attenuation curves at all redshifts (e.g., Salmon et al. 2016; Buat et al. 2018; Salim et al. 2018). With the shape of the attenuation curve being strongly dependent on the relative geometry of stars, ionized gas, and dust (Salim & Narayanan 2020), from the disturbed morphologies observed at higher redshifts, we can only expect important variations there as well (e.g., Faisst et al. 2017). However, due to the great difficulty in measuring them and given the sparsity of the data available, our knowledge of attenuation curves beyond z = 4 remains limited. In effect, most observational studies on the attenuation properties of distant galaxies tend to concentrate on redshifts between 2 and 4 (e.g., Noll et al. 2009b; Buat et al. 2012, 2019; Reddy et al. 2012, 2015; Shivaei et al. 2015; Álvarez-Márquez et al. 2016; Salmon et al. 2016; Fudamoto et al. 2017, 2020b; Lo Faro et al. 2017; Álvarez-Márquez et al. 2019; Reddy et al. 2018; Koprowski et al. 2020). There is only a handful of examples at higher redshift (Capak et al. 2015; Scoville et al. 2015; Bouwens et al. 2016; Barisic et al. 2017; Koprowski et al. 2018). Because of the inherent limits of the observations, studies based on numerical simulations of galaxies at very high redshift (e.g., Mancini et al. 2016; Cullen et al. 2017; Di Mascia et al. 2021) are an important source of information. However, they lead to contrasted results, finding both flat (Cullen et al. 2017) and steep (Mancini et al. 2016) attenuation curves.
Citation: Buat et al. 2005
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Citation: Seibert et al. 2005
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