On the importance of using appropriate spectral models to derive physical properties of galaxies at 0.7<z<2.8

TL;DR

This study demonstrates that using advanced spectral models significantly improves the accuracy of derived galaxy physical properties, such as stellar mass and SFR, at redshifts 0.7 to 2.8, compared to classical methods.

Contribution

The paper introduces a Bayesian spectral fitting approach with realistic star-formation histories, dust attenuation, and nebular emission, showing improvements over classical assumptions.

Findings

Classical methods overestimate stellar mass by ~0.1 dex.

Classical methods underestimate SFR by ~0.6 dex.

Advanced models reduce uncertainties and biases in galaxy property estimates.

Abstract

Interpreting observations of distant galaxies in terms of constraints on physical parameters - such as stellar mass, star-formation rate (SFR) and dust optical depth - requires spectral synthesis modelling. We analyse the reliability of these physical parameters as determined under commonly adopted `classical' assumptions: star-formation histories assumed to be exponentially declining functions of time, a simple dust law and no emission-line contribution. Improved modelling techniques and data quality now allow us to use a more sophisticated approach, including realistic star-formation histories, combined with modern prescriptions for dust attenuation and nebular emission (Pacifici et al. 2012). We present a Bayesian analysis of the spectra and multi-wavelength photometry of 1048 galaxies from the 3D-HST survey in the redshift range 0.7<z<2.8 and in the stellar mass range 9<log(M/Mo)<12. We find that, using the classical spectral library, stellar masses are systematically overestimated (~0.1 dex) and SFRs are systematically underestimated (~0.6 dex) relative to our more sophisticated approach. We also find that the simultaneous fit of photometric fluxes and emission-line equivalent widths helps break a degeneracy between SFR and optical depth of the dust, reducing the uncertainties on these parameters. Finally, we show how the biases of classical approaches can affect the correlation between stellar mass and SFR for star-forming galaxies (the `Star-Formation Main Sequence'). We conclude that the normalization, slope and scatter of this relation strongly depend on the adopted approach and demonstrate that the classical, oversimplified approach cannot recover the true distribution of stellar mass and SFR.