Marvin: A Toolkit for Streamlined Access and Visualization of the SDSS-IV MaNGA Data Set

The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, one of three core programs of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), is producing a massive, high-dimensional integral field spectroscopic data set. However, leveraging the MaNGA data set to address key questi...

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Published inarXiv.org
Main Authors Cherinka, Brian, Andrews, Brett H, Sánchez-Gallego, José, Brownstein, Joel, Argudo-Fernández, María, Blanton, Michael, Bundy, Kevin, Jones, Amy, Masters, Karen, Law, David R, Rowlands, Kate, Weijmans, Anne-Marie, Westfall, Kyle, Yan, Renbin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.12.2018
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ISSN2331-8422
DOI10.48550/arxiv.1812.03833

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Abstract The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, one of three core programs of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), is producing a massive, high-dimensional integral field spectroscopic data set. However, leveraging the MaNGA data set to address key questions about galaxy formation presents serious data-related challenges due to the combination of its spatially inter-connected measurements and sheer volume. For each galaxy, the MaNGA pipelines produce relatively large data files to preserve the spatial correlations of the spectra and measurements, but this comes at the expense of storing the data set in a coarsely-chunked manner. The coarse chunking and total volume of the data make it time-consuming to download and curate locally-stored data. Thus, accessing, querying, visually exploring, and performing statistical analyses across the whole data set at a fine-grained scale is extremely challenging using just FITS files. To overcome these challenges, we have developed \marvin: a toolkit consisting of a Python package, Application Programming Interface (API), and web application utilizing a remote database. \marvin's robust and sustainable design minimizes maintenance, while facilitating user-contributed extensions such as high level analysis code. Finally, we are in the process of abstracting out \marvin's core functionality into a separate product so that it can serve as a foundation for others to develop \marvin-like systems for new science applications.
AbstractList The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, one of three core programs of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), is producing a massive, high-dimensional integral field spectroscopic data set. However, leveraging the MaNGA data set to address key questions about galaxy formation presents serious data-related challenges due to the combination of its spatially inter-connected measurements and sheer volume. For each galaxy, the MaNGA pipelines produce relatively large data files to preserve the spatial correlations of the spectra and measurements, but this comes at the expense of storing the data set in a coarsely-chunked manner. The coarse chunking and total volume of the data make it time-consuming to download and curate locally-stored data. Thus, accessing, querying, visually exploring, and performing statistical analyses across the whole data set at a fine-grained scale is extremely challenging using just FITS files. To overcome these challenges, we have developed a toolkit consisting of a Python package, Application Programming Interface (API), and web application utilizing a remote database. 's robust and sustainable design minimizes maintenance, while facilitating user-contributed extensions such as high level analysis code. Finally, we are in the process of abstracting out 's core functionality into a separate product so that it can serve as a foundation for others to develop -like systems for new science applications.
The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, one of three core programs of the fourth-generation Sloan Digital Sky Survey (SDSS-IV), is producing a massive, high-dimensional integral field spectroscopic data set. However, leveraging the MaNGA data set to address key questions about galaxy formation presents serious data-related challenges due to the combination of its spatially inter-connected measurements and sheer volume. For each galaxy, the MaNGA pipelines produce relatively large data files to preserve the spatial correlations of the spectra and measurements, but this comes at the expense of storing the data set in a coarsely-chunked manner. The coarse chunking and total volume of the data make it time-consuming to download and curate locally-stored data. Thus, accessing, querying, visually exploring, and performing statistical analyses across the whole data set at a fine-grained scale is extremely challenging using just FITS files. To overcome these challenges, we have developed \marvin: a toolkit consisting of a Python package, Application Programming Interface (API), and web application utilizing a remote database. \marvin's robust and sustainable design minimizes maintenance, while facilitating user-contributed extensions such as high level analysis code. Finally, we are in the process of abstracting out \marvin's core functionality into a separate product so that it can serve as a foundation for others to develop \marvin-like systems for new science applications.
Author Andrews, Brett H
Argudo-Fernández, María
Cherinka, Brian
Blanton, Michael
Westfall, Kyle
Rowlands, Kate
Brownstein, Joel
Weijmans, Anne-Marie
Yan, Renbin
Jones, Amy
Bundy, Kevin
Sánchez-Gallego, José
Law, David R
Masters, Karen
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BackLink https://doi.org/10.48550/arXiv.1812.03833$$DView paper in arXiv
https://doi.org/10.3847/1538-3881/ab2634$$DView published paper (Access to full text may be restricted)
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SubjectTerms Application programming interface
Applications programs
Correlation analysis
Datasets
Downloading
Galactic evolution
Galaxies
Mapping
Physics - Astrophysics of Galaxies
Physics - Instrumentation and Methods for Astrophysics
Programming languages
Sky surveys (astronomy)
Star & galaxy formation
Statistical analysis
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Title Marvin: A Toolkit for Streamlined Access and Visualization of the SDSS-IV MaNGA Data Set
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