Chrome Extension
WeChat Mini Program
Use on ChatGLM

The APOGEE Value-Added Catalogue of Galactic Globular Cluster Stars

Monthly Notices of the Royal Astronomical Society(2024)SCI 2区

Liverpool John Moores Univ | Texas Christian Univ | Univ Andres Bello | Inst Astrofis Canarias | Univ Notre Dame | Princeton Univ | Univ Utah | Rutgers State Univ | Univ Catolica Norte | Malmo Univ | Univ Bernardo OHiggins | Univ Virginia | Eotvos Lorand Univ | Univ Colorado

Cited 0|Views8
Abstract
ABSTRACT We introduce the Sloan Digital Sky Survey (SDSS)/ Apache Point Observatory Galactic Evolution Experiment (APOGEE) value-added catalogue of Galactic globular cluster (GC) stars. The catalogue is the result of a critical search of the APOGEE Data Release 17 (DR17) catalogue for candidate members of all known Galactic GCs. Candidate members are assigned to various GCs on the basis of position in the sky, proper motion, and radial velocity. The catalogue contains a total of 7737 entries for 6422 unique stars associated with 72 Galactic GCs. Full APOGEE DR17 information is provided, including radial velocities and abundances for up to 20 elements. Membership probabilities estimated on the basis of precision radial velocities are made available. Comparisons with chemical compositions derived from the GALactic Archaeology with HERMES (GALAH) survey, as well as optical values from the literature, show good agreement. This catalogue represents a significant increase in the public data base of GC star chemical compositions and kinematics, providing a massive homogeneous data set that will enable a variety of studies. The catalogue in fits format is available for public download from the SDSS-IV DR17 value-added catalogue website.
More
Translated text
Key words
catalogues,stars: abundances,Galaxy: abundances,globular clusters: general
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Raymond G. Carlberg,Carl J. Grillmair
2022

被引用6 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest