Chemodynamic subpopulations of the Carina dwarf galaxy

Kordopatis, G.; Amorisco, Nicola C.; Evans, N. W.; Gilmore, G.; Koposov, S. E.

doi:10.1093/mnras/stw073

Cited by 26 publications

(40 citation statements)

References 61 publications

(87 reference statements)

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“…The mean radial velocity of our sample of 32 stars (all of which are radial-velocity members) is 224.8 ± 1.0 km s −1 , with dispersion 5.65 ± 0.71 km s −1 . We note that the dispersion agrees well with those of Walker et al (2009a), within the limits, but is somewhat lower than those of 10.4 ± 1, 7.6 ± 0.5, and 8.5 ± 0.8 km s −1 , reported by Kordopatis et al (2016) for their metal-poor, intermediate-metallicity, and metal-richer RGB stars. For completeness, we also note that we find no dependence of velocity dispersion on metallicity in our relatively small sample of stars.…”

Section: Radial Velocitiessupporting

confidence: 84%

“…It utilizes the power of the ESO VLT FLAMES-UVES system in UVES-Fiber mode (Pasquini et al 2002), which permits simultaneous observation of a large number of stars at intermediate resolution (R ∼ 6500), via a system of 130 fibres, together with a smaller number at high resolution (R ∼ 47000), via eight fibres. The analysis of the lower resolution sample has been reported elsewhere (Kordopatis et al 2016). The obvious advantage of the higher resolution capability is that it enables insight into the chemical abundances of considerably more elements (∼ 20) than is possible at the lower resolution.…”

Section: Observational Materialsmentioning

confidence: 99%

“…For these three panels a Gaussian kernel of 0.15 dex, appropriate to the accuracy of the abundance analyses, has been adopted. Panel (d) contains the histogram of the Carina MDF from Starkenburg et al (2010, their Figure 13) obtained from analysis of the Ca II IR triplet data of Koch et al (2006);and panel (e) presents the MDF of Kordopatis et al (2016, their Figure 7). the present sample; from our analysis of data from Shetrone et al (2003) and Venn et al (2012); and from our analysis of the data of Lemasle et al (2012), respectively.…”

Section: Appendix: Cross Identification and Averaged Abundances For Tmentioning

confidence: 99%

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The Populations of Carina. II. Chemical Enrichment^*

Norris

Yong

Venn

et al. 2017

ApJS

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Chemical abundances are presented for 19 elements in a sample of 63 red giants in the Carina dwarf spheroidal galaxy (dSph), based on homogeneous 1D/LTE model atmosphere analyses of our own observations (32 stars) and data available in the literature (a further 31 independent stars). The Approximate color-magnitude-diagram age estimates are presented for the sample and, together with our chemical abundances, compared with the results of our previous synthetic CMD analysis, which reported the details of Carina's four well-defined populations.We searched for the Na-O anti-correlation universally reported in the Galaxy's globular clusters, and confirm that this phenomenon does not exist in Carina. We also found that one of the 32 stars in our sample has an extremely enhanced lithium abundance -A(Li) NLTE = +3.36, consistent with membership of the ∼1% group of Li-rich stars in dSph described by Kirby et al. (2012).

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Section: Radial Velocitiessupporting

confidence: 84%

Section: Observational Materialsmentioning

confidence: 99%

Section: Appendix: Cross Identification and Averaged Abundances For Tmentioning

confidence: 99%

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The Populations of Carina. II. Chemical Enrichment^*

Norris

Yong

Venn

et al. 2017

ApJS

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“…This allows us to obtain the absolute magnitudes in several photometric bands as in Kordopatis et al (2011bKordopatis et al ( , 2013cKordopatis et al ( , 2015, and an estimation of the age of the stars as in Kordopatis et al (2016); Magrini et al (2017). The distances are then obtained using the distance modulus in the J band, and assuming A J = 0.709 E(B − V) (Schlafly & Finkbeiner 2011), where E(B − V) are the Schlegel extinctions towards each line-of-sight.…”

Section: Radial Velocities Atmospheric Parameters and Spectroscopic mentioning

confidence: 99%

An artificial neural network to discover hypervelocity stars: candidates in Gaia DR1/TGAS

Marchetti

Rossi

Kordopatis

et al. 2017

Monthly Notices of the Royal Astronomical Society

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The paucity of hypervelocity stars (HVSs) known to date has severely hampered their potential to investigate the stellar population of the Galactic Centre and the Galactic Potential. The first Gaia data release (DR1, 2016 September 14) gives an opportunity to increase the current sample. The challenge is the disparity between the expected number of hypervelocity stars and that of bound background stars. We have applied a novel data mining algorithm based on machine learning techniques, an artificial neural network, to the Tycho-Gaia astrometric solution (TGAS) catalogue. With no pre-selection of data, we could exclude immediately ∼ 99% of the stars in the catalogue and find 80 candidates with more than 90% predicted probability to be HVSs, based only on their position, proper motions, and parallax. We have cross-checked our findings with other spectroscopic surveys, determining radial velocities for 30 and spectroscopic distances for 5 candidates. In addition, follow-up observations have been carried out at the Isaac Newton Telescope for 22 stars, for which we obtained radial velocities and distance estimates. We discover 14 stars with a total velocity in the Galactic rest frame > 400 km s −1 , and 5 of these have a probability > 50% of being unbound from the Milky Way. Tracing back their orbits in different Galactic potential models we find one possible unbound HVS with v ∼ 520 km s −1 , 5 bound HVSs, and, notably, 5 runaway stars with median velocity between 400 and 780 km s −1 . At the moment, uncertainties in the distance estimates and ages are too large to confirm the nature of our candidates by narrowing down their ejection location, and we wait for future Gaia releases to validate the quality of our sample. This test successfully demonstrates the feasibility of our new data mining routine.

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“…Large spectroscopic surveys with accurate velocity and metallicity measurements have shown that the stellar kinematics is distinct between the metal-poor and metalrich populations (e.g. Tolstoy et al 2004;Battaglia et al 2006Battaglia et al , 2008McConnachie, Peñarrubia & Navarro 2007;Walker & Peñarrubia 2011;Hendricks et al 2014;Kordopatis et al 2016).…”

mentioning

confidence: 99%

Multiple chemodynamic stellar populations of the Ursa Minor dwarf spheroidal galaxy

Pace

Kaplinghat

Kirby

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We present a Bayesian method to identify multiple (chemodynamic) stellar populations in dwarf spheroidal galaxies (dSphs) using velocity, metallicity, and positional stellar data without the assumption of spherical symmetry. We apply this method to a new Keck/Deep Imaging Multi-Object Spectrograph (DEIMOS) spectroscopic survey of the Ursa Minor (UMi) dSph. We identify 892 likely members, making this the largest UMi sample with line-of-sight velocity and metallicity measurements. Our Bayesian method detects two distinct chemodynamic populations with high significance (in logarithmic Bayes factor, ln B ∼ 33). The metal-rich ([Fe/H] = −2.05 ± 0.03) population is kinematically colder (radial velocity dispersion of $\sigma _v=4.9_{-1.0}^{+0.8} \, \mathrm{km} \, \mathrm{s}^{-1}$) and more centrally concentrated than the metal-poor ($[{\rm Fe/H}]=-2.29_{-0.06}^{+0.05}$) and kinematically hotter population ($\sigma _v =11.5_{-0.8}^{+0.9}\, \mathrm{km} \, \mathrm{s}^{-1}$). Furthermore, we apply the same analysis to an independent Multiple Mirror Telescope (MMT)/Hectochelle data set and confirm the existence of two chemodynamic populations in UMi. In both data sets, the metal-rich population is significantly flattened (ϵ = 0.75 ± 0.03) and the metal-poor population is closer to spherical ($\epsilon =0.33_{-0.09}^{+0.12}$). Despite the presence of two populations, we are able to robustly estimate the slope of the dynamical mass profile. We found hints for prolate rotation of order ${\sim}2 \, \mathrm{km} \, \mathrm{s}^{-1}$ in the MMT data set, but further observations are required to verify this. The flattened metal-rich population invalidates assumptions built into simple dynamical mass estimators, so we computed new astrophysical dark matter annihilation (J) and decay profiles based on the rounder, hotter metal-poor population and inferred $\log _{10}{(J(0{^{\circ}_{.}}5)/{\rm GeV^{2} \, cm^{-5}})}\approx 19.1$ for the Keck data set. Our results paint a more complex picture of the evolution of UMi than previously discussed.

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Chemodynamic subpopulations of the Carina dwarf galaxy

Cited by 26 publications

References 61 publications

The Populations of Carina. II. Chemical Enrichment^*

The Populations of Carina. II. Chemical Enrichment^*

An artificial neural network to discover hypervelocity stars: candidates in Gaia DR1/TGAS

Multiple chemodynamic stellar populations of the Ursa Minor dwarf spheroidal galaxy

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Chemodynamic subpopulations of the Carina dwarf galaxy

Cited by 26 publications

References 61 publications

The Populations of Carina. II. Chemical Enrichment*

The Populations of Carina. II. Chemical Enrichment*

An artificial neural network to discover hypervelocity stars: candidates in Gaia DR1/TGAS

Multiple chemodynamic stellar populations of the Ursa Minor dwarf spheroidal galaxy

Contact Info

Product

Resources

About

The Populations of Carina. II. Chemical Enrichment^*

The Populations of Carina. II. Chemical Enrichment^*