Stellar Mass Distribution and Star Formation History of the Galactic Disk Revealed by Mono-age Stellar Populations from LAMOST

Xiang, Maosheng; Shi, Jianrong; Liu, Xiaowei; Yuan, Haibo; Chen, Bingqiu; Huang, Yang; Wang, Chun; Wu, Yaqian; Tian, Zhijia; Huo, Zhi-Ying; Zhang, Huawei; Zhang, Meng

doi:10.3847/1538-4365/aad237

Cited by 44 publications

(41 citation statements)

References 132 publications

(223 reference statements)

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“…We then add 0.12 dex of noise to the ages, to emulate their age histograms. We find that the best fit parameters obtained from the Ting18 sample (the ages we have focused on during the analysis) are more consistent with the trends in Xiang et al (2018) than those from the other age sets, at young ages (where it matters and where the data are constraining). The model predictions and those of Xiang et al (2018) differ significantly at large ages: we under predict the number of old stars.…”

Section: Comparisons With Literature Resultsmentioning

confidence: 53%

“…We find that the best fit parameters obtained from the Ting18 sample (the ages we have focused on during the analysis) are more consistent with the trends in Xiang et al (2018) than those from the other age sets, at young ages (where it matters and where the data are constraining). The model predictions and those of Xiang et al (2018) differ significantly at large ages: we under predict the number of old stars. This could come from that our model is mostly constrained by young stars, and that we only model the low-α disk, whereas Xiang et al (2018) derived these age histograms considering all stars.…”

Section: Comparisons With Literature Resultsmentioning

confidence: 53%

“…The model predictions and those of Xiang et al (2018) differ significantly at large ages: we under predict the number of old stars. This could come from that our model is mostly constrained by young stars, and that we only model the low-α disk, whereas Xiang et al (2018) derived these age histograms considering all stars.…”

Section: Comparisons With Literature Resultsmentioning

confidence: 78%

“…To compare, we simulate this procedure. We sample stars of all populations (not just red clump) from our best fit evolutionary model and select them in Galactocentric radius bins as in Xiang et al (2018). We then add 0.12 dex of noise to the ages, to emulate their age histograms.…”

Section: Comparisons With Literature Resultsmentioning

confidence: 99%

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The Inside-out Growth of the Galactic Disk

Frankel

Sanders

Rix

et al. 2019

ApJ

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We quantify the inside-out growth of the Milky Way's low-α stellar disk, modelling the ages, metallicities and Galactocentric radii of APOGEE red clump stars with 6 < R < 13 kpc. The current stellar distribution differs significantly from that expected from the star formation history due to the redistribution of stars through radial orbit mixing. We propose and fit a global model for the Milky Way disk, specified by an inside-out star formation history, radial orbit mixing, and an empirical, parametric model for its chemical evolution. We account for the spatially complex survey selection function, and find that the model fits all data well. We find distinct inside-out growth of the Milky Way disk; the best fit model implies that the half-mass radius of the Milky Way disk has grown by 43% over the last 7 Gyr. Yet, such inside-out growth still results in present-day age gradient weaker than 0.1 Gyr kpc −1 . Our model predicts the half-mass and half-light sizes of the Galactic disk at earlier epochs, which can be compared to the observed redshift -size relations of disk galaxies. We show that radial orbit migration can reconcile the distinct disk-size evolution with redshift, also expected from cosmological simulations, with the modest present-day age gradients seen in the Milky Way and other galaxies.

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Section: Comparisons With Literature Resultsmentioning

confidence: 53%

Section: Comparisons With Literature Resultsmentioning

confidence: 53%

Section: Comparisons With Literature Resultsmentioning

confidence: 78%

Section: Comparisons With Literature Resultsmentioning

confidence: 99%

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The Inside-out Growth of the Galactic Disk

Frankel

Sanders

Rix

et al. 2019

ApJ

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“…RAVE (Steinmetz et al 2006), SEGUE (Yanny et al 2009), LAMOST Zhao et al 2012), Gaia-ESO (Gilmore et al 2012), GALAH (De Silva et al 2015), APOGEE (Majewski et al 2017), and the Gaia Radial Velocity Spectrometer (Cropper et al 2018); as well as email: mxiang@mpia.de * Hubble fellow upcoming surveys such as SDSS-V (Kollmeier et al 2017), 4MOST (Feltzing et al 2018;de Jong et al 2019), and WEAVE (Dalton et al 2014). These spectroscopic surveys, combined with the astrometric and photometric information from the Gaia mission (Gaia Collaboration et al 2016Collaboration et al , 2018, make it possible for us to obtain precise and accurate information for millions of stars in phase space or orbit space, along with estimates of age, mass, metallicity, and abundances for many elements, providing unprecedented opportunities to unravel the assemblage and evolution history of our Galaxy (see e.g., Rix & Bovy 2013;Ting, Conroy & Goodman 2015;Xiang et al 2017aXiang et al , 2018Frankel et al 2018;Bland-Hawthorn et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Abundance Estimates for 16 Elements in 6 Million Stars from LAMOST DR5 Low-Resolution Spectra

Xiang¹,

Ting²,

Rix³

et al. 2019

ApJS

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We present the determination of stellar parameters and individual elemental abundances for 6 million stars from ∼8 million low-resolution (R ∼ 1800) spectra from LAMOST DR5. This is based on a modeling approach that we dub T he Data-Driven P ayne (DD-P ayne), which inherits essential ingredients from both The Payne (Ting et al. 2019) and T he Cannon (Ness et al. 2015). It is a data-driven model that incorporates constraints from theoretical spectral models to ensure the derived abundance estimates are physically sensible. Stars in LAMOST DR5 that are in common with either GALAH DR2 or APOGEE DR14 are used to train a model that delivers stellar parameters (T eff , log g, V mic ) and abundances for 16 elements (C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, and Ba) when applied to LAMOST spectra. Cross-validation and repeat observations suggest that, for S/N pix ≥ 50, the typical internal abundance precision is 0.03-0.1 dex for the majority of these elements, with 0.2-0.3 dex for Cu and Ba, and the internal precision of T eff and log g is better than 30 K and 0.07 dex, respectively. Abundance systematics at the ∼0.1 dex level are present in these estimates, but are inherited from the high-resolution surveys' training labels. For some elements, GALAH provides more robust training labels, for others, APOGEE. We provide flags to guide the quality of the label determination and to identify binary/multiple stars in LAMOST DR5. The abundance catalogs are publicly accessible via http://dr5.lamost.org/doc/vac.

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