ner_tags: O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O B-TextGarbage I-TextGarbage I-TextGarbage I-TextGarbage O O O O O O O B-TextGarbage O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-TextGarbage I-TextGarbage O B-TextGarbage O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O B-Citation I-Citation O O B-TextGarbage O O O O B-TextGarbage O O O O O O O O O O O O O O O O B-TextGarbage O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O O O O O O O O B-Model O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O O O O O O O O B-Model I-Model B-Model O O O O O O O O O O B-CelestialObject I-CelestialObject O O O O O O O O O O O O B-Model O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
tokens: form the previous iteration, resulting in a final cleanest sample containing 224 245 QSOs with (for which we have Ly free photometry in the -, -, and -bands), 135,953 with (where we can use the -band), and 77 633 QSO with , where we can use all the SDSS bands. We have an estimate of the intrinsic colours , and an estimate of the reddening for each QSO that is retained by the redshift constraints. Even though the unobscured QSO intrinsic colours are computed independently for each band pair, we do obtain consistent results across the band pairs, i.e. holds for all the bands , , and , over all the redshifts considered. Working with the column density maps as an estimate of the extinction instead of the gives very similar estimates of , and is independent of any dust modelling, so this means we did not translate potential dust modelling systematics into our QSO estimates. Fig. B.3 QSO magnitude increase per unit of dust extinction as a function of the QSO redshift. We use an extinction curve with . The and band curves are shown in a thinner trace for and , the redshifts at which the intergalactic Ly line can affect the photometry in these bands. In order to compare the estimate with a QSO estimate, we need to derive a QSO extinction from the different colour excess . We proceed as follows. For a given QSO spectrum and extinction curve shape, we can compute the SDSS magnitude increase per dust extinction for . These ratios depend on the assumed extinction curve and QSO spectral shape, and therefore on the QSO redshift. Using the QSO composite spectrum of Vanden Berk et al. (2001) and the extinction curve presented by Fitzpatrick (1999) parametrized via , we compute the ratios : Figure B.3 shows as a function of the QSO redshift , for the different bands and . Even though the QSO intrinsic colours are strong functions of its redshift, the extinction curves are smooth enough that is mostly redshift independent. Using the extinction curves with (which was also used to constrain the optical properties of the grains used in the DL model), for each redshift , we define: and Finally, for each QSO, we define its as the average of the values for all the band pairs that are allowed by its redshift . Appendix C: Impact of the CIB anisotropies and instrumental noise on the parameter estimation Fig. C.1 Comparison of the original and recovered dust mass under CIBA and instrumental noise simulation in the diffuse ISM for a resolution. Colour corresponds to the density of points (see Fig. 5 ). We study the impact of CIB anisotropies (CIBA) and instrumental (stochastic) noise in our mass estimates in the diffuse ISM (where their effect should be the largest). We simulate data by adding CIBA and instrumental noise to DL SEDs, and fit them with the same technique as we use to fit the observed data. The results quantify the deviations of the recovered parameters from the original ones. We start by a family of four DL SEDs with and , a typical , and . We