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
  • [41] AUTOMATED ERROR IDENTIFICATION DURING NONDESTRUCTIVE TESTING OF PIPELINES FOR STRENGTH
    Kornuta, Jeffrey A.
    Thorsson, Solver, I
    Gibbs, Jonathan
    Veloo, Peter
    Rovella, Troy
    PROCEEDINGS OF THE ASME 2020 13TH INTERNATIONAL PIPELINE CONFERENCE (IPC2020), VOL 1, 2020,
  • [42] NEAT: a framework for building fully automated NGS pipelines and analyses
    Schorderet, Patrick
    BMC BIOINFORMATICS, 2016, 17
  • [43] PIPELINE SCHEDULING .1. AUTOMATED SCHEDULING SYSTEM FOR PIPELINES
    BAXTER, W
    GOPALANI, A
    MURRAY, E
    OIL & GAS JOURNAL, 1981, 79 (39) : 323 - 325
  • [44] Automated Construction of Continuous Delivery Pipelines from Architecture Models
    Aydin, Selin
    Steffens, Andreas
    Lichter, Horst
    2021 28TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2021), 2021, : 306 - 316
  • [45] Automated pipelines for rapid evaluation during cryoEM data acquisition
    Mendez, Joshua H.
    Chua, Eugene Y. D.
    Paraan, Mohammadreza
    Potter, Clinton S.
    Carragher, Bridget
    CURRENT OPINION IN STRUCTURAL BIOLOGY, 2023, 83
  • [46] ReClean: Reinforcement Learning for Automated Data Cleaning in ML Pipelines
    Abdelaal, Mohamed
    Yayak, Anil Bora
    Klede, Kai
    Schoening, Harald
    2024 IEEE 40TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOP, ICDEW, 2024, : 324 - 330
  • [47] Automated spectral analysis -: III:: Application to in vivo proton MR spectroscopy and spectroscopic imaging
    Soher, BJ
    Young, K
    Govindaraju, V
    Maudsley, AA
    MAGNETIC RESONANCE IN MEDICINE, 1998, 40 (06) : 822 - 831
  • [48] Population-Average Brain Templates and Application to Automated Voxel-Wise Analysis Pipelines for Cynomolgus Macaque
    Ouyang, Fubing
    Chen, Xinran
    Liang, Jiahui
    Li, Jianle
    Jiang, Zimu
    Chen, Yicong
    Yan, Zhicong
    Zeng, Jinsheng
    Xing, Shihui
    NEUROINFORMATICS, 2022, 20 (03) : 613 - 626
  • [49] Comparison of Automated Pipelines for Nanopore 16S Sequencing Data Analysis of Human Gut Microbiota Samples
    Andrei, Lobiuc
    Roxana, Gheorghita
    Mihai, Dimian
    Pavel, Ionut
    Mihai, Covasa
    2021 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB 2021), 9TH EDITION, 2021,
  • [50] Population-Average Brain Templates and Application to Automated Voxel-Wise Analysis Pipelines for Cynomolgus Macaque
    Fubing Ouyang
    Xinran Chen
    Jiahui Liang
    Jianle Li
    Zimu Jiang
    Yicong Chen
    Zhicong Yan
    Jinsheng Zeng
    Shihui Xing
    Neuroinformatics, 2022, 20 : 613 - 626