Systematic Difference between Ionized and Molecular Gas Velocity Dispersions in z ∼ 1–2 Disks and Local Analogs
Systematic Difference between Ionized and Molecular Gas Velocity Dispersions in z ∼ 1–2 Disks and Local Analogs
复制标题
DOI:
10.3847/1538-4357/abd5b9
复制
发表时间:
2021-01
期刊:
影响因子:
--
通讯作者:
M. Girard;D. Fisher;A. Bolatto;R. Abraham;R. Bassett;K. Glazebrook;R. Herrera-Camus;E. Jiménez;L. Lenkić;D. Obreschkow
中科院分区:
文献类型:
--
作者:
M. Girard;D. Fisher;A. Bolatto;R. Abraham;R. Bassett;K. Glazebrook;R. Herrera-Camus;E. Jiménez;L. Lenkić;D. Obreschkow
We compare the molecular and ionized gas velocity dispersions of nine nearby turbulent disks, analogs to high-redshift galaxies, from the DYNAMO sample using new Atacama Large Millimeter/submillimeter Array and GMOS/Gemini observations. We combine our sample with 12 galaxies at z ∼ 0.5–2.5 from the literature. We find that the resolved velocity dispersion is systematically lower by a factor 2.45 ± 0.38 for the molecular gas compared to the ionized gas, after correcting for thermal broadening. This offset is constant within the galaxy disks and indicates the coexistence of a thin molecular gas disk and a thick ionized one. This result has a direct impact on the Toomre Q and pressure derived in galaxies. We obtain pressures ∼0.22 dex lower on average when using the molecular gas velocity dispersion, σ 0,mol. We find that σ 0,mol increases with gas fraction and star formation rate. We also obtain an increase with redshift and show that the EAGLE and FIRE simulations overall overestimate σ 0,mol at high redshift. Our results suggest that efforts to compare the kinematics of gas using ionized gas as a proxy for the total gas may overestimate the velocity dispersion by a significant amount in galaxies at the peak of cosmic star formation. When using the molecular gas as a tracer, our sample is not consistent with predictions from star formation models with constant efficiency, even when including transport as a source of turbulence. Feedback models with variable star formation efficiency, ϵ ff, and/or feedback efficiency, p */m *, better predict our observations.