2020
DOI: 10.48550/arxiv.2009.01935
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Gas-phase Metallicity as a Diagnostic of the Drivers of Star-formation on Different Spatial Scales

Enci Wang,
Simon J. Lilly

Abstract: This work mainly includes two parts: the theoretical predictions and observational results on the correlation of SFR and gas-phase metallicity Z gas . We first predict the correlation between SFR, cold gas mass and Z gas in the gas-regulator frame that the instantaneous SFR is regulated by the interplay between inflow, outflow and star formation. The mean SFR is determined by mean inflow rate and mass-loading factor λ, while the mean Z gas is determined by the metallicity of inflow gas Z 0 and the effective yi… Show more

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Cited by 3 publications
(4 citation statements)
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References 100 publications
(194 reference statements)
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“…Consistent with the analyis of the Illustris-TNG simulations presented in Torrey et al (2018), our analysis of the EAGLE simulations indicates that important timescales for these processes are of the order of a few Gyr or longer, which is reflected in how long galaxies stay above/below the MZR, on average. There timescales are also consistent with the recent inference presented in Wang & Lilly (2020a), based on the residuals measured in observational data.…”
Section: Discussionsupporting
confidence: 91%
See 1 more Smart Citation
“…Consistent with the analyis of the Illustris-TNG simulations presented in Torrey et al (2018), our analysis of the EAGLE simulations indicates that important timescales for these processes are of the order of a few Gyr or longer, which is reflected in how long galaxies stay above/below the MZR, on average. There timescales are also consistent with the recent inference presented in Wang & Lilly (2020a), based on the residuals measured in observational data.…”
Section: Discussionsupporting
confidence: 91%
“…Theoretical work has also been used to study the connection betwen the MZR and variables that are not readily observable. For example, using simple idealised galaxy evolution models, Forbes et al (2014) and Wang & Lilly (2020a) study how fluctuations in gas inflows rates can drive the MZR scatter, emphasing the importance of the relationship between the fluctuation timescale for inflows and the gas consumption timescale in the ISM. De Lucia et al (2020b) use a more complex semi-analytic model of galaxy formation to study the physical drivers of the MZR relation, and arrive at the conclusion that gas accretion drives the MZR scatter, with galaxies below the MZR relation being driven by sustained periods of gaseous inflow.…”
mentioning
confidence: 99%
“…[40] and [20] measured the oxygen metallicity in H ii regions in our Galaxy and found metallicity variations over the overall radial gradient of ∼ 0.3 dex over large scales, and smaller along the direction of the long bar. [41] recently found that ∼ 0.5 dex variations of gasphase metallicity on scales of 100 pc, observed from strong-emission lines diagnostics in Very Large Telescope/Multi Unit Spectroscopic Explorer (MUSE) data of nearby galaxies, positively correlate with the variations in star-formation rates, which they interpret as due to time variations of the star-formation efficiency and including gas-infall in their gas-regulated theoretical models. With similar observational techniques, [42] found that the metallicity of H ii regions in nearby galaxies show deviations of up to 0.2-0.3 dex from the radial gradients, and these variations increase at smaller scales [43].…”
mentioning
confidence: 99%
“…These data have stimulated interest in the relationship between the gas-phase oxygen abundances, stellar mass, star formation rate and gas content of galaxies on kpc scales (e.g. Barrera-Ballesteros * schaefer@mpa-garching.mpg.de Mingozzi et al 2020;Teklu et al 2020;Wang & Lilly 2020). By analogy to the global mass metallicity relation (Tremonti et al 2004), the oxygen abundance has been shown to be sensitive to the local stellar mass surface density (Σ * ), tracing the integrated star formation history on local scales in a galaxy, as well as the presence of outflows (e.g Barrera-Ballesteros et al 2018) and inflows (Lian et al 2019;Schaefer et al 2019).…”
Section: Introductionmentioning
confidence: 99%