|
other query modes : |
Identifier query |
Coordinate query |
Criteria query |
Reference query |
Basic query |
Script submission |
TAP |
Output options |
Object types |
Help |
| HE 0240-0807 , the SIMBAD biblio (18 results) | C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST00:53:45 |
| Bibcode/DOI | Score |
in Title|Abstract| Keywords |
in a table | in teXt, Caption, ... | Nb occurence | Nb objects in ref |
Citations (from ADS) |
Title | First 3 Authors |
|---|---|---|---|---|---|---|---|---|---|
|
2005A&A...439..129B |
15 | D | 1 | 252 | 422 | The Hamburg/ESO R-process enhanced star survey (HERES). II. Spectroscopic analysis of the survey sample. | BARKLEM P.S., CHRISTLIEB N., BEERS T.C., et al. | ||
|
2007ApJS..168..128B |
15 | D | 1852 | 38 | Broadband UBVRCIC photometry of horizontal-branch and metal-poor candidates from the HK and Hamburg/ESO surveys. I. | BEERS T.C., FLYNN C., ROSSI S., et al. | |||
|
2011A&A...528A..92Z |
15 | D | 1 | 254 | 10 | The Hamburg/ESO R-process Enhanced Star survey (HERES). VI. The Galactic chemical evolution of silicon. | ZHANG L., KARLSSON T., CHRISTLIEB N., et al. | ||
| 2012A&A...537A.118R | 15 | D | 2 | 112 | 9 | The Hamburg/ESO R-process Enhanced Star survey (HERES). VII. Thorium abundances in metal-poor stars. | REN J., CHRISTLIEB N. and ZHAO G. | ||
| 2014A&A...565A..93S | 906 | D | X C | 23 | 13 | 30 | High-resolution abundance analysis of very metal-poor r-I stars. | SIQUEIRA MELLO C., HILL V., BARBUY B., et al. | |
|
2014ApJ...797...21P |
15 | D | 2 | 606 | 226 | Carbon-enhanced metal-poor star frequencies in the galaxy: corrections for the effect of evolutionary status on carbon abundances. | PLACCO V.M., FREBEL A., BEERS T.C., et al. | ||
|
2016A&A...591A.118S |
16 | D | 1 | 31408 | 141 | The PASTEL catalogue: 2016 version. | SOUBIRAN C., LE CAMPION J.-F., BROUILLET N., et al. | ||
|
2018AJ....156..179R |
16 | D | 1 | 90 | 63 | Kinematics of highly r-process-enhanced field stars: evidence for an accretion origin and detection of several groups from disrupted satellites. | ROEDERER I.U., HATTORI K. and VALLURI M. | ||
|
2019ApJ...882...40J |
16 | D | 2 | 210 | 41 | The lanthanide fraction distribution in metal-poor stars: a test of neutron star mergers as the dominant r-process site. | JI A.P., DROUT M.R. and HANSEN T.T. | ||
|
2022A&A...663A...4S |
17 | D | 1 | 14206 | 7 | Assessment of [Fe/H] determinations for FGK stars in spectroscopic surveys. | SOUBIRAN C., BROUILLET N. and CASAMIQUELA L. | ||
| 2022A&A...663A..70F | 17 | D | 1 | 47 | 17 | Correlations of r-process elements in very metal-poor stars as clues to their nucleosynthesis sites. | FAROUQI K., THIELEMANN F.-K., ROSSWOG S., et al. | ||
|
2022A&A...665A..10L |
17 | D | 1 | 56 | 8 | Chemical Evolution of R-process Elements in Stars (CERES). I. Stellar parameters and chemical abundances from Na to Zr. | LOMBARDO L., BONIFACIO P., FRANCOIS P., et al. | ||
|
2023ApJ...943...23S |
17 | D | 1 | 1792 | 5 | The R-Process Alliance: Chemodynamically Tagged Groups. II. An Extended Sample of Halo r-process-enhanced Stars. | SHANK D., BEERS T.C., PLACCO V.M., et al. | ||
|
2023ApJ...946...48H |
17 | D | 2 | 181 | 5 | Finding r-II Sibling Stars in the Milky Way with the Greedy Optimistic Clustering Algorithm. | HATTORI K., OKUNO A. and ROEDERER I.U. | ||
| 2024A&A...691A.220F | 18 | D | 2 | 54 | ~ | Chemical Evolution of R-process Elements in Stars (CERES) II. The impact of stellar evolution and rotation on light and heavy elements. | FERNANDES DE MELO R., LOMBARDO L., ALENCASTRO PULS A., et al. | ||
| 2024A&A...692A.130B | 18 | D | 1 | 100 | ~ | Cosmological insights into the early accretion of r-process-enhanced stars II. Dynamical identification of lost members of Reticulum II. | BERCZIK P., ISHCHENKO M., SOBODAR O., et al. | ||
| 2025A&A...693A.294A | 19 | D | 1 | 58 | ~ | Chemical Evolution of R-process Elements in Stars (CERES) IV. An observational run-up of the third r-process peak with Hf, Os, Ir, and Pt. | ALENCASTRO PULS A., KUSKE J., HANSEN C.J., et al. | ||
| 2025ApJ...992...36V | 19 | D | 1 | 42 | ~ | Classifying Metal-poor Stars with Machine Learning Using Nucleosynthesis Calculations. | VASSH N., WANG Y., WOLOSHYN R.M., et al. |