Automated pipelines for spectroscopic analysis

被引:5
|
作者
Allende Prieto, C. [1 ,2 ]
机构
[1] Inst Astrofis Canarias, C Via Lactea S-N, Tenerife 38205, Spain
[2] Univ La Laguna, Dept Astrofis, E-38206 Tenerife, Spain
关键词
catalogs; methods: data analysis; surveys; techniques: spectroscopic; SDSS OPTICAL SPECTROSCOPY; VELOCITY EXPERIMENT RAVE; DIGITAL SKY SURVEY; GAIA-ESO SURVEY; ATMOSPHERIC PARAMETERS; STARS; ABUNDANCES; TELESCOPE; SPECTRA; SEGUE;
D O I
10.1002/asna.201612382
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but also leaves some glaring holes in an otherwise complete data set. The radial velocities measured with the on-board high-resolution spectrograph will only reach some 10% of the full sample of stars with astrometry and photometry from the mission, and detailed chemical information will be obtained for less than 1%. Teams all over the world are organizing large-scale projects to provide complementary radial velocities and chemistry, since this can now be done very efficiently from the ground thanks to large and mid-size telescopes with a wide field-of-view and multi-object spectrographs. As a result, automated data processing is taking an ever increasing relevance, and the concept is applying to many more areas, from targeting to analysis. In this paper, I provide a quick overview of recent, ongoing, and upcoming spectroscopic surveys, and the strategies adopted in their automated analysis pipelines. (C) 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
引用
收藏
页码:837 / 843
页数:7
相关论文
共 50 条
  • [31] A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques
    Song, Young Kyoung
    Hong, Sang Hee
    Eo, Soeun
    Shim, Won Joon
    Marine Pollution Bulletin, 2021, 173
  • [32] A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques
    Song, Young Kyoung
    Hong, Sang Hee
    Eo, Soeun
    Shim, Won Joon
    MARINE POLLUTION BULLETIN, 2021, 173
  • [33] Pairwise running of automated crystallographic model-building pipelines
    Alharbi, Emad
    Calinescu, Radu
    Cowtan, Kevin
    ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2020, 76 : 814 - 823
  • [34] Automated evolutionary approach for the design of composite machine learning pipelines
    Nikitin, Nikolay O.
    Vychuzhanin, Pavel
    Sarafanov, Mikhail
    Polonskaia, Iana S.
    Revin, Ilia
    V. Barabanova, Irina
    Maximov, Gleb
    Kalyuzhnaya, Anna, V
    Boukhanovsky, Alexander
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2022, 127 : 109 - 125
  • [35] Automated Vision Systems for Condition Assessment of Sewer and Water Pipelines
    Rayhana, Rakiba
    Jiao, Yutong
    Zaji, Amirhossein
    Liu, Zheng
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 18 (04) : 1861 - 1878
  • [36] ARTIFACT: Architecture for Automated Generation of Distributed Information Extraction Pipelines
    Sildatke, Michael
    Karwanni, Hendrik
    Kraft, Bodo
    Zundorf, Albert
    ICEIS: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS - VOL 2, 2022, : 17 - 28
  • [37] Predicting the performance of automated crystallographic model-building pipelines
    Alharbi, Emad
    Bond, Paul
    Calinescu, Radu
    Cowtan, Kevin
    ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2021, 77 : 1591 - 1601
  • [38] AutoSkull: Learning-Based Skull Estimation for Automated Pipelines
    Milojevic, Aleksandar
    Peter, Daniel
    Huber, Niko B.
    Azevedo, Luis
    Latyshev, Andrei
    Sailer, Irena
    Gross, Markus
    Thomaszewski, Bernhard
    Solenthaler, Barbara
    Gozcu, Baran
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT VII, 2024, 15007 : 109 - 118
  • [39] Implementation of Automated Pipelines to Generate Knowledge on Challenging Biological Queries
    Vazquez, Noe
    DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2019, 801 : 426 - 430
  • [40] NEAT: a framework for building fully automated NGS pipelines and analyses
    Patrick Schorderet
    BMC Bioinformatics, 17