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The data analysis pipeline for the SDSS-IV MaNGA IFU galaxy survey: overview

Research output: Contribution to journalArticle

  • Kyle B. Westfall
  • Michele Cappellari
  • Matthew A. Bershady
  • Kevin Bundy
  • Francesco Belfiore
  • Xihan Ji
  • David R. Law
  • Adam Schaefer
  • Shravan Shetty
  • Christy A. Tremonti
  • Renbin Yan
  • Brett H. Andrews
  • Joel R. Brownstein
  • Brian Cherinka
  • Lodovico Coccato
  • Niv Drory
  • Taniya Parikh
  • José R. Sánchez-Gallego
  • Anne-Marie Weijmans
  • Jorge Barrera-Ballesteros
  • Cheng Du
  • Daniel Goddard
  • Niu Li
  • Karen Masters
  • Héctor Javier Ibarra Medel
  • Sebastián F. Sánchez
  • Meng Yang
  • Zheng Zheng
  • Shuang Zhou
The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is currently acquiring integral-field spectroscopy for the largest sample of galaxies to date. By 2020, the MaNGA Survey — which is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV) — will have observed a statistically representative sample of 104 galaxies in the local universe (z ≲ 0.15). In addition to a robust data-reduction pipeline (DRP), MaNGA has developed a data-analysis pipeline (DAP) that provides higher-level data products. To accompany the first public release of its code base and data products, we provide an overview of the MaNGA DAP, including its software design, workflow, measurement procedures and algorithms, performance, and output data model. In conjunction with our companion paper (Belfiore et al.), we also assess the DAP output provided for 4718 observations of 4648 unique galaxies in the recent SDSS Data Release 15 (DR15). These analysis products focus on measurements that are close to the data and require minimal model-based assumptions. Namely, we provide stellar kinematics (velocity and velocity dispersion), emission-line properties (kinematics, fluxes, and equivalent widths), and spectral indices (e.g., D4000 and the Lick indices). We find that the DAP provides robust measurements and errors for the vast majority (>99%) of analyzed spectra. We summarize assessments of the precision and accuracy of our measurements as a function of signal-to-noise. We also provide specific guidance to users regarding the limitations of the data. The MaNGA DAP software is publicly available and we encourage community involvement in its development.
Original languageEnglish
Article number231
Number of pages57
JournalThe Astronomical Journal
Volume158
Issue number6
DOIs
Publication statusPublished - 18 Nov 2019

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  • Westfall_2019_AJ_158_231

    Rights statement: Kyle B. Westfall et al 2019 AJ 158 231. © 2019 The American Astronomical Society. All rights reserved. Reproduced by permission of the AAS.

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