Scientific Analyst II, Steward Observatory (Part Time)

University of Arizona

  • Tucson, AZ
  • 3 days ago

    Highlights

    Develop an all-sky NUV map based on the established FUV processing methodology and perform the same comparative analyses conducted for the FUV dataset, including evaluation against CNM structure maps and the enhanced TD1 catalog. Compare these datasets with an enhanced TD1 catalog to determine distances to dust structures through the application of machine learning, Monte Carlo radiative transfer calculations, and statistical analysis methods.

    Numbers & Facts

    LocationTucson, AZ

    Description

    • Refine and enhance the analysis of GALEX FUV images by performing point source subtraction, infilling, sky background matching, and mosaicing into larger fields.
    • Develop and integrate GALEX FUV data into the HiPS format to support all-sky accessibility, as well as assisting in any uploads or uses for other projects. Assist with data uploads to the MAST archive and incorporate the dataset into ongoing 3D Galactic dust mapping efforts. Extend the analysis to the currently untapped GALEX NUV dataset, which will require significant changes to the existing code base.
    • Conduct comparative analyses between the completed all sky map in the FUV and maps of high-latitude CNM structures generated from HI4PI and GALFA-HI data using a modified CNM detection algorithm. Compare these datasets with an enhanced TD1 catalog to determine distances to dust structures through the application of machine learning, Monte Carlo radiative transfer calculations, and statistical analysis methods.
    • Develop an all-sky NUV map based on the established FUV processing methodology and perform the same comparative analyses conducted for the FUV dataset, including evaluation against CNM structure maps and the enhanced TD1 catalog.
    • Prepare scientific papers describing the research findings and develop comprehensive documentation detailing the data processing methodology, workflows, and procedures used to create the dataset for submission to the MAST archive.

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