Example code and data for "Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique"

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dc.contributor.authorTiwari, Maitraiyee
dc.contributor.authorKievit, Rens
dc.contributor.authorKabanovic, Slawa
dc.contributor.authorBonne, Lars
dc.contributor.authorFalasca, F.
dc.contributor.authorGuevara, Cristian
dc.contributor.authorHiggins, Ronan
dc.contributor.authorJusten, M.
dc.contributor.authorKarim, Ramsey
dc.contributor.authorPabst, Cornelia
dc.contributor.authorPound, Marc W.
dc.contributor.authorSchneider, Nicola
dc.contributor.authorSimon, R.
dc.contributor.authorStutzki, Jurgen
dc.contributor.authorWolfire, Mark
dc.contributor.authorTielens, Alexander G. G. M.
dc.date.accessioned2023-08-28T19:19:15Z
dc.date.available2023-08-28T19:19:15Z
dc.date.issued2023
dc.descriptionSee README.md.
dc.description.abstractWe present a python software repository implementing the PyGMMis (Melchior & Goudling 2018) method to astronomical data cubes of velocity resolved line observations. This implementation is described extensively in Tiwari et al. 2023, ApJ. An example is included in /example/ containing the SOFIA data of RCW120 used in Tiwari et al. 2023, ApJ, along with example scripts describing the full implementation of our code. The majority of parameter tweaking can be performed within 'rcw120-params.txt' which is continuously called during the procedure. A full description of the code and how to use it is in README.md (markdown file).
dc.description.sponsorshipThis study was based on observations made with the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is jointly operated by the Universities Space Research Association Inc. (USRA), under NASA contract NNA17BF53C, and the Deutsches SOFIA Institut (DSI), under DLR contract 50OK0901 to the University of Stuttgart. upGREAT is a development by the MPI für Radioastronomie and the KOSMA/University of Cologne, in cooperation with the DLR Institut für Optische Sensorsysteme. N.S. acknowledges support from the FEEDBACK-plus project that is supported by the BMWI via DLR, Project number 50OR2217 (FEEDBACK-plus). S.K. acknowledges support from the Orion-Legacy project that is is supported by the BMWI via DLR, project number 50OR2311. Publication costs were provided by NASA through the award SOF070077 issued by USRA.
dc.identifierhttps://doi.org/10.13016/dspace/p9m2-9n5w
dc.identifier.urihttp://hdl.handle.net/1903/30423
dc.language.isoen_US
dc.relation.isAvailableAtAstronomyen_us
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectmachine learning
dc.subjectstar-forming regions
dc.subjectinterstellar medium
dc.subjectPython programming language
dc.titleExample code and data for "Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique"
dc.typeSoftware
local.equitableAccessSubmissionNo

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Explanation of Python code and technique used in "Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique" by M. Tiwari et al. 2023, Astrophysical Journal
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