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We develop and implement an inclination-dependent attenuation prescriptionfor spectral energy distribution (SED) fitting and study its impact on derivedstar-formation histories. We apply our prescription within the SED fitting codeLightning to a clean sample of 82, z=0.21-1.35 disk-dominated galaxies in theGreat Observatories Origins Deep Survey North and South fields. To compare ourinclination-dependent attenuation prescription with more traditional fittingprescriptions, we also fit the SEDs with the inclination-independent Calzettiet al. (2000) attenuation curve. From this comparison, we find that fits to asubset of 58, z < 0.7 galaxies in our sample, utilizing the Calzetti et al.(2000) prescription, recover similar trends with inclination as theinclination-dependent fits for the far-UV-band attenuation and recentstar-formation rates. However, we find a difference between prescriptions inthe optical attenuation (AV) that is strongly correlated with inclination(p-value < 10^-11). For more face-on galaxies, with i < 50 deg, (edge-on, i =90 deg), the average derived AV is 0.31 +\- 0.11 magnitudes lower (0.56 +\-0.16 magnitudes higher) for the inclination-dependent model compared totraditional methods. Further, the ratio of stellar masses between prescriptionsalso has a significant (p-value < 10^-2) trend with inclination. For i = 0-65deg, stellar masses are systematically consistent between fits, withlog(Mstar_inc/Mstar_Calzetti) = -0.05 +/- 0.03 dex and scatter of 0.11 dex.However, for i = 80-90 deg, derived stellar masses are lower for the Calzettiet al. (2000) fits by an average factor of 0.17 +\- 0.03 dex and scatter of0.13 dex. Therefore, these results suggest that SED fitting assuming theCalzetti et al. (2000) attenuation law potentially underestimates stellarmasses in highly inclined disk-dominated galaxies.

astrophysical journal/the astrophysical journal

On the Impact of Inclination-Dependent Attenuation on Derived Star  Formation Histories: Results from Disk Galaxies in the Great Observatories  Origins Deep Survey Fields