2010
DOI: 10.1088/0004-637x/714/2/1096
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The Galactic Structure and Chemical Evolution Traced by the Population of Planetary Nebulae

Abstract: Planetary nebulae (PNe) derive from the evolution of ∼1-8 M ⊙ mass stars, corresponding to a wide range of progenitor ages, thus are essential probes of the chemical evolution of galaxies, and indispensable to constrain the results from chemical models. We use an extended and homogeneous data set of Galactic PNe to study the metallicity gradients and the Galactic structure and evolution. The most up-to-date abundances, distances (calibrated with Magellanic Cloud PNe), and other parameters have been employed, t… Show more

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Cited by 157 publications
(233 citation statements)
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References 71 publications
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“…Recent models of chemical and galaxy evolution predict that radial migration can wipe out gradients of older populations, causing younger populations to have steeper observed gradients (e.g., Roškar et al 2008;Loebman et al 2011;Kubryk et al 2013). Our result of the steepening of the gradient with time are compatible with these models and the results from Stanghellini & Haywood (2010). Frinchaboy et al (2013) measured a single radial fit of −0.09 dex kpc −1 for their sample of open clusters observed with APOGEE, but find that it is potentially better fit with two components: a steeper gradient of −0.2 dex kpc −1 between 7.9 < R < 10 kpc, and a flat radial gradient for clusters with R > 10 kpc.…”
Section: Intermediate Radiisupporting
confidence: 87%
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“…Recent models of chemical and galaxy evolution predict that radial migration can wipe out gradients of older populations, causing younger populations to have steeper observed gradients (e.g., Roškar et al 2008;Loebman et al 2011;Kubryk et al 2013). Our result of the steepening of the gradient with time are compatible with these models and the results from Stanghellini & Haywood (2010). Frinchaboy et al (2013) measured a single radial fit of −0.09 dex kpc −1 for their sample of open clusters observed with APOGEE, but find that it is potentially better fit with two components: a steeper gradient of −0.2 dex kpc −1 between 7.9 < R < 10 kpc, and a flat radial gradient for clusters with R > 10 kpc.…”
Section: Intermediate Radiisupporting
confidence: 87%
“…There is disagreement in the literature over this result. Stanghellini & Haywood (2010) observed planetary nebula across a range of ages and find that older populations have shallower metallicity gradients than younger populations. However, in a similar study of a different set of planetary nebula, Maciel & Costa (2009) find the opposite result; they observed the gradient to flatten with time, with older populations having steeper gradients.…”
Section: Intermediate Radiimentioning
confidence: 99%
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“…The problem is that each type of objects shows a different evolution. Thus, PNs which gave a steep radial gradient for a time ∼8 Gyr compared with the one corresponding to the present time given by Hii regions, give now, following the most recent findings, a flat evolution along the time; that is, the radial gradient for O is basically the same from 5-6 Gyr ago until now , or even has steepen slightly on time Stanghellini & Haywood (2010). A radial gradient constant along the time is also obtained by Magrini et al (2016) for M33, M31, M81 and NGC300, comparing their radial distributions of Oxygen abundances given by Hii regions and PN, finding no evidences of evolution of the radial gradient with time.…”
Section: Abundances In the Mwgsupporting
confidence: 76%
“…One of the major sources of this confusion is the unsettled PNe distance determination problem allied with the extreme paucity of nebulae at large galactocentric distances. In fact, in the most recent works, those by Henry et al (2010) and Stanghellini & Haywood (2010), only two PNe with D GC > 17 kpc are included in each sample (K3-64, K3-70, and M1-9, K3-66, respectively), all four PNe having mediocrily determined O abundances (quoted errors from 0.2 to 0.5 dex) and distances. However, these distant nebulae are obviously critical for the gradient determination.…”
Section: Discussionmentioning
confidence: 99%