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dc.contributor.authorCherinka, Brian
dc.contributor.authorAndrews, Brett H.
dc.contributor.authorSánchez-Gallego, José
dc.contributor.authorBrownstein, Joel
dc.contributor.authorArgudo-Fernández, María
dc.contributor.authorBlanton, Michael
dc.contributor.authorBundy, Kevin
dc.contributor.authorJones, Amy
dc.contributor.authorMasters, Karen
dc.contributor.authorLaw, David R.
dc.contributor.authorWeijmans, Anne-Marie
dc.contributor.authorWestfall, Kyle
dc.contributor.authorYan, Renbin
dc.date.accessioned2019-08-07T15:30:02Z
dc.date.available2019-08-07T15:30:02Z
dc.date.issued2019-07-19
dc.identifier.citationCherinka , B , Andrews , B H , Sánchez-Gallego , J , Brownstein , J , Argudo-Fernández , M , Blanton , M , Bundy , K , Jones , A , Masters , K , Law , D R , Weijmans , A-M , Westfall , K & Yan , R 2019 , ' Marvin : a tool kit for streamlined access and visualization of the SDSS-IV MaNGA data set ' , Astrophysical Journal , vol. 158 , no. 2 , 74 . https://doi.org/10.3847/1538-3881/ab2634en
dc.identifier.issn0004-637X
dc.identifier.otherPURE: 256942584
dc.identifier.otherPURE UUID: ecbc595c-c550-45d1-9b1d-d5ba4750c337
dc.identifier.otherBibCode: 2018arXiv181203833C
dc.identifier.otherORCID: /0000-0002-5908-6852/work/60427616
dc.identifier.otherWOS: 000476604700004
dc.identifier.otherScopus: 85072034850
dc.identifier.urihttp://hdl.handle.net/10023/18264
dc.description.abstractThe 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 interconnected 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 coarse units or "chunks." This coarse chunking and the 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, and web application utilizing a remote database. Marvin allows users to seamlessly work with MaNGA data by abstracting both remote and local (on-disk) interactions to behind-the-scenes data-handling functions. Combining this capability with additional processing and querying tools, users can create powerful Python workflows that are easy to import and share. Marvin's web application uses these tools to enable "point-and-click" examination of data cubes and derived maps, as well as search queries for all publicly released MaNGA galaxies. Marvin's robust and sustainable design minimizes maintenance, while facilitating user-contributed extensions such as high-level analysis code.
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofAstrophysical Journalen
dc.rightsCopyright © 2019. The American Astronomical Society. All rights reserved. This work is made available online in accordance with the publisher’s policies. This is the final published version of the work, which was originally published at: https://doi.org/10.3847/1538-3881/ab2634en
dc.subjectAstonomical databases: miscellaneousen
dc.subjectMethods: data analysisen
dc.subjectSurveysen
dc.subjectQB Astronomyen
dc.subjectQC Physicsen
dc.subject3rd-DASen
dc.subject.lccQBen
dc.subject.lccQCen
dc.titleMarvin : a tool kit for streamlined access and visualization of the SDSS-IV MaNGA data seten
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Physics and Astronomyen
dc.identifier.doihttps://doi.org/10.3847/1538-3881/ab2634
dc.description.statusPeer revieweden
dc.identifier.urlhttp://adsabs.harvard.edu/abs/2018arXiv181203833Cen


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