13 research outputs found
Gaia Data Release 2. Kinematics of globular clusters and dwarf galaxies around the Milky Way
AIMS: The goal of this paper is to demonstrate the outstanding quality of the second data release of the Gaia mission and its power for constraining many different aspects of the dynamics of the satellites of the Milky Way. We focus here on determining the proper motions of 75 Galactic globular clusters, nine dwarf spheroidal galaxies, one ultra-faint system, and the Large and Small Magellanic Clouds. METHODS: Using data extracted from the Gaia archive, we derived the proper motions and parallaxes for these systems, as well as their uncertainties. We demonstrate that the errors, statistical and systematic, are relatively well understood. We integrated the orbits of these objects in three different Galactic potentials, and characterised their properties. We present the derived proper motions, space velocities, and characteristic orbital parameters in various tables to facilitate their use by the astronomical community. RESULTS: Our limited and straightforward analyses have allowed us for example to (i) determine absolute and very precise proper motions for globular clusters; (ii) detect clear rotation signatures in the proper motions of at least five globular clusters; (iii) show that the satellites of the Milky Way are all on high-inclination orbits, but that they do not share a single plane of motion; (iv) derive a lower limit for the mass of the Milky Way of 9.1{_â.ââșâ¶Â·ÂČ} x 10ÂčÂč Mâ based on the assumption that the Leo I dwarf spheroidal is bound; (v) derive a rotation curve for the Large Magellanic Cloud based solely on proper motions that is competitive with line-of-sight velocity curves, now using many orders of magnitude more sources; and (vi) unveil the dynamical effect of the bar on the motions of stars in the Large Magellanic Cloud. CONCLUSIONS: All these results highlight the incredible power of the Gaia astrometric mission, and in particular of its second data release
Gaia Data Release 2 Variable stars in the colour-absolute magnitude diagram
Context. The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G âČ 21 mag.
Aims. We showcase stellar variability in the Galactic colour-absolute magnitude diagram (CaMD). We focus on pulsating, eruptive, and cataclysmic variables, as well as on stars that exhibit variability that is due to rotation and eclipses.
Methods. We describe the locations of variable star classes, variable object fractions, and typical variability amplitudes throughout the CaMD and show how variability-related changes in colour and brightness induce âmotionsâ. To do this, we use 22 months of calibrated photometric, spectro-photometric, and astrometric Gaia data of stars with a significant parallax. To ensure that a large variety of variable star classes populate the CaMD, we crossmatched Gaia sources with known variable stars. We also used the statistics and variability detection modules of the Gaia variability pipeline. Corrections for interstellar extinction are not implemented in this article.
Results. Gaia enables the first investigation of Galactic variable star populations in the CaMD on a similar, if not larger, scale as was previously done in the Magellanic Clouds. Although the observed colours are not corrected for reddening, distinct regions are visible in which variable stars occur. We determine variable star fractions to within the current detection thresholds of Gaia. Finally, we report the most complete description of variability-induced motion within the CaMD to date.
Conclusions. Gaia enables novel insights into variability phenomena for an unprecedented number of stars, which will benefit the understanding of stellar astrophysics. The CaMD of Galactic variable stars provides crucial information on physical origins of variability in a way that has previously only been accessible for Galactic star clusters or external galaxies. Future Gaia data releases will enable significant improvements over this preview by providing longer time series, more accurate astrometry, and additional data types (time series BP and RP spectra, RVS spectra, and radial velocities), all for much larger samples of stars
Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.
Background: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. Results: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000?10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. Conclusions: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.Made available in DSpace on 2018-08-08T00:50:51Z (GMT). No. of bitstreams: 1
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Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus
VizieR Online Data Catalog: Gaia DR2. Variable stars in CMD (Gaia Collaboration+, 2019)
Time series in the G, BP, and RP bands of the selected field-of-view transits for 224 sources that are not published in Gaia DR2, but are plotted in Fig. 11. An animated version of Fig. 11 is provided online and at https://www.cosmos.esa.int/web/gaia/gaiadr2_cu7. (2 data files)
VizieR Online Data Catalog: 46 open clusters GaiaDR2 HR diagrams (Gaia Collaboration, 2018)
VizieR On-line Data Catalog: J/A+A/616/A10. Originally published in: 2018A&A...616A..10GWe have determined the membership of 46 open clusters. For the nine clusters within 250pc we determined optimised parallaxes based on the combined information extracted from the measured parallax and proper motion values. These clusters are : in Tables A1a & A3: alphaPer, Blanco1, ComaBer, Hyades, IC2391, IC2602, NGC2451A, Pleiades, Praesepe. The remaining 37 clusters are in Table A1b & A4: Coll140, IC4651, IC4665, IC4725, IC4756, NGC0188, NGC0752, NGC0869, NGC0884, NGC1039, NGC1901, NGC2158, NGC2168, NGC2232, NGC2323, NGC2360, NGC2422, NGC2423, NGC2437, NGC2447, NGC2516, NGC2547, NGC2548, NGC2682, NGC3228, NGC3532, NGC6025, NGC6281, NGC6405, NGC6475, NGC6633, NGC6774, NGC6793, NGC7092, Stock2, Trump02, Trump10. (4 data files)
VizieR Online Data Catalog: Gaia DR2 sources in GC and dSph (Gaia Collaboration+, 2018)
VizieR On-line Data Catalog: J/A+A/616/A12. Originally published in: 2018A&A...616A..12GThe files contains lists of possible members of each of the objects (75 globular clusters, 9 dwarf spheroidal galaxies, the Bootes I UFD, the LMC and SMC). The stars in these lists have been selected and used to determine the astrometric parameters of the corresponding objects following either the procedures described in Sec. 2.1 (for the clusters and dwarfs) or in Sec. 2.2 (for the LMC and SMC). The first column is the "source_id" as given by Gaia, the ra and declination of the star in degrees, and its G-band magnitude (known as "photgmean_mag" in the Gaia archive). (2 data files)
VizieR Online Data Catalog: 46 open clusters GaiaDR2 HR diagrams (Gaia Collaboration, 2018)
VizieR On-line Data Catalog: J/A+A/616/A10. Originally published in: 2018A&A...616A..10GWe have determined the membership of 46 open clusters. For the nine clusters within 250pc we determined optimised parallaxes based on the combined information extracted from the measured parallax and proper motion values. These clusters are : in Tables A1a & A3: alphaPer, Blanco1, ComaBer, Hyades, IC2391, IC2602, NGC2451A, Pleiades, Praesepe. The remaining 37 clusters are in Table A1b & A4: Coll140, IC4651, IC4665, IC4725, IC4756, NGC0188, NGC0752, NGC0869, NGC0884, NGC1039, NGC1901, NGC2158, NGC2168, NGC2232, NGC2323, NGC2360, NGC2422, NGC2423, NGC2437, NGC2447, NGC2516, NGC2547, NGC2548, NGC2682, NGC3228, NGC3532, NGC6025, NGC6281, NGC6405, NGC6475, NGC6633, NGC6774, NGC6793, NGC7092, Stock2, Trump02, Trump10. (4 data files)