Re-assessing the stellar population scaling relations of the galaxies in the Local Universe
D. Mattolini, S. Zibetti, A. R. Gallazzi, L. Scholz-Diaz, J. Pratesi
Published: 2025/9/4
Abstract
Local galaxies follow scaling relations between mass and stellar population properties such as age and metallicity, which encode key information on their evolutionary histories. We revise these relations using the largest spectroscopic dataset from SDSS DR7 (0.005<z<0.22), improved Stellar Population Synthesis (SPS) models, aperture-corrections, and statistical weights to account for selection biases. In a Bayesian framework, we estimate stellar masses, mean ages, and metallicities by comparing spectral indices and photometry with composite SPS models. We adopt updated prescriptions for Star Formation Histories (SFH) and Chemical Enrichment Histories (CEH), while also testing different models and priors. We measure light-weighted ages for 354,977 galaxies (SNR>10) and metallicities for 89,852 galaxies (SNR>20), analyzing their dependence on stellar mass. Key findings include: i) A revised bimodal mass-age distribution, with a young sequence at low mass and an old sequence at high mass, partly overlapping in mass and transitioning at 10^10.8 solar masses. ii) A Mass-Metallicity Relation (MZR) shifted upwards by 0.2 dex relative to previous works. Aperture corrections lower masses, ages, and metallicities in a mass-dependent way, enhancing the young sequence and steepening the MZR. iii) Using Halpha-based SFRs, we found that while star-forming/young and quiescent/old correspondences generally hold, exceptions exist for many galaxies. Quiescent galaxies show a flatter, less scattered MZR than star-forming ones, with convergence at high mass. iv) SPS assumptions strongly affect our results, particularly SFHs and CEHs. These revised relations provide new benchmarks for galaxy evolution studies and simulations. Systematic uncertainties of 0.15 dex may arise from aperture biases and SPS modelling choices, highlighting the need for consistent assumptions when comparing observations and models.