Non-Gaussian LSF shapes bias kinematic extraction from spectra; matching the LSF of templates to the target reduces dispersion bias below 1%.
Assessing the Jeans Anisotropic Multi-Gaussian Expansion method with the Illustris simulation
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abstract
We assess the effectiveness of the Jeans-Anisotropic-MGE (JAM) technique with a state-of-the-art cosmological hydrodynamic simulation, the Illustris project. We perform JAM modelling on 1413 simulated galaxies with stellar mass M^* > 10^{10}M_{sun}, and construct an axisymmetric dynamical model for each galaxy. Combined with a Markov Chain Monte Carlo (MCMC) simulation, we recover the projected root-mean-square velocity (V_rms) field of the stellar component, and investigate constraints on the stellar mass-to-light ratio, M^*/L, and the fraction of dark matter f_{DM} within 2.5 effective radii (R_e). We find that the enclosed total mass within 2.5 R_e is well constrained to within 10%. However, there is a degeneracy between the dark matter and stellar components with correspondingly larger individual errors. The 1 sigma scatter in the recovered M^*/L is 30-40% of the true value. The accuracy of the recovery of M^*/L depends on the triaxial shape of a galaxy. There is no significant bias for oblate galaxies, while for prolate galaxies the JAM-recovered stellar mass is on average 18% higher than the input values. We also find that higher image resolutions alleviate the dark matter and stellar mass degeneracy and yield systematically better parameter recovery.
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The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models
Non-Gaussian LSF shapes bias kinematic extraction from spectra; matching the LSF of templates to the target reduces dispersion bias below 1%.