Using mock spectra based on Vazdekis/MILES library fitted within the wavelength region 3600–7350 Å, we analyse the bias and scatter on the resulting physical parameters induced by the choice of fitting algorithms and observational uncertainties, but avoid effects of those model uncertainties. We consider two full-spectrum fitting codes: PPXF and STARLIGHT, in fitting for stellar population age, metallicity, mass-to-light ratio, and dust extinction. With PPXF, we find that both the bias μ in the population parameters and the scatter σ in the recovered logarithmic values follows the expected trend μ ∝ σ ∝ 1/(S/N). The bias increases for younger ages and systematically makes recovered ages older, M*/Lr larger and metallicities lower than the true values. For reference, at S/N = 30, and for the worst case (t = 108 yr), the bias is 0.06 dex in M*/Lr, 0.03 dex in both age and [M/H]. There is no significant dependence on either E(B − V) or the shape of the error spectrum. Moreover, the results are consistent for both our 1-SSP (simple stellar population) and 2-SSP tests. With the STARLIGHT algorithm, we find trends similar to PPXF, when the input E(B − V) < 0.2 mag. However, with larger input E(B − V), the biases of the output parameter do not converge to zero even at the highest S/N and are strongly affected by the shape of the error spectra. This effect is particularly dramatic for youngest age (t = 108 yr), for which all population parameters can be strongly different from the input values, with significantly underestimated dust extinction and [M/H], and larger ages and M*/Lr. Results degrade when moving from our 1-SSP to the 2-SSP tests. The STARLIGHT convergence to the true values can be improved by increasing Markov Chains and annealing loops to the ‘slow mode’. For the same input spectrum, PPXF is about two order of magnitudes faster than STARLIGHT’s ‘default mode’ and about three order of magnitude faster than STARLIGHT’s ‘slow mode’.
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This work is supported by the National Natural Science Foundation of China (NSFC) under grant number 11473032 (JG), 11333003 (SM), 11390372 (SM, YL), and 11690024 (YL). RY acknowledges support by National Science Foundation grant AST-1715898. MC acknowledges support from a Royal Society University Research Fellowship.
Ge, Junqiang; Yan, Renbin; Cappellari, Michele; Mao, Shude; Li, Hongyu; and Lu, Youjun, "Recovering Stellar Population Parameters via Two Full-Spectrum Fitting Algorithms in the Absence of Model Uncertainties" (2018). Physics and Astronomy Faculty Publications. 636.