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26 April 2024 |
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Article overview
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Marvin: A Toolkit for Streamlined Access and Visualization of the SDSS-IV MaNGA Data Set | | Brian Cherinka
; Brett H. Andrews
; José Sánchez-Gallego
; Joel Brownstein
; María Argudo-Fernández
; Michael Blanton
; Kevin Bundy
; Amy Jones
; Karen Masters
; David R. Law
; Kate Rowlands
; Anne-Marie Weijmans
; Kyle Westfall
; Renbin Yan
; | | Date: |
6 Dec 2018 | | 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. | | Source: | arXiv, 1812.3833 | | Services: | Forum | Review | PDF | Favorites | |
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