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| dustmaps | |
|---|---|
| Name | dustmaps |
| Title | dustmaps |
| Author | Maarten A. B. Hendriks |
| Released | 2016 |
| Language | Python |
| License | BSD-3-Clause |
dustmaps is a Python library and dataset collection for retrieving and querying three-dimensional interstellar extinction and reddening maps. It provides programmatic access to three-dimensional dust models and two-dimensional extinction maps used in observational astronomy, enabling cross-matching with surveys and instruments. The project is widely used in studies working with data from major observatories and missions.
dustmaps is designed to bridge survey catalogs and three-dimensional interstellar extinction models used by researchers working with Sloan Digital Sky Survey, Gaia, Two Micron All Sky Survey, Wide-field Infrared Survey Explorer, and other large-scale projects. It supplies parsers and query interfaces for well-known maps such as those produced by teams using Pan-STARRS1, APOGEE, Planck, COBE, and targeted studies around objects like Orion and the Galactic Center. By integrating with scientific stacks that include NumPy, SciPy, Astropy, and matplotlib, dustmaps allows reproducible extinction corrections in pipelines for instruments like Hubble Space Telescope and James Webb Space Telescope.
The initial codebase emerged in the mid-2010s as part of efforts collating three-dimensional extinction products created by collaborations between research groups at institutions such as Harvard University, University of Cambridge, Max Planck Society, and Princeton University. Early datasets incorporated models from teams linked to surveys like Pan-STARRS1 and spectroscopic programs such as APOGEE. Over time, contributions from developers with affiliations to observatories such as Space Telescope Science Institute and data centers like Centre de Données astronomiques de Strasbourg expanded support to include maps derived from missions like Planck (spacecraft) and legacy products from COBE. Releases tracked improvements to I/O performance, support for coordinate systems used by International Astronomical Union, and packaging for dependency managers used by Python Software Foundation ecosystems.
dustmaps serves as an access layer to underlying extinction maps derived from varied methods: stellar photometric inversion in projects tied to Pan-STARRS1 and 2MASS, spectroscopic extinction estimates from APOGEE and LAMOST, and far-infrared thermal emission modeling from missions like Planck (spacecraft) and IRAS. The library implements coordinate conversions compatible with standards from International Astronomical Union and handles distance estimators aligned with parallax data from Gaia releases. It reads formats produced by pipelines used at centers including Space Telescope Science Institute and data archives such as Vizier. Methodologies supported include Bayesian line-of-sight inversion approaches developed in academic groups at University of California, Berkeley and regularized tomographic reconstructions from teams associated with Max Planck Institute for Astronomy.
Researchers use dustmaps in studies of stellar populations from catalogs like Sloan Digital Sky Survey and Gaia, in characterizing extinction toward objects observed by Hubble Space Telescope and James Webb Space Telescope, and in cross-matching photometry from surveys such as Pan-STARRS1 and WISE. It underpins analyses of star-formation regions in Orion, extinction toward the Galactic Center, and foreground corrections for extragalactic studies involving Sloan Digital Sky Survey and Hubble Deep Field. Observational programs at facilities including European Southern Observatory and Keck Observatory use dustmaps-derived extinction corrections in spectroscopic modeling and target selection. The tool is incorporated into pipelines for stellar parameter inference in projects associated with Apache Point Observatory and population synthesis efforts at universities like University of Cambridge.
Limitations of products accessible through dustmaps reflect underlying assumptions in the source maps: finite angular resolution limits from instruments such as Planck (spacecraft) and IRAS, distance uncertainty propagated from Gaia parallaxes, and model degeneracies inherent to photometric inversion used by teams behind Pan-STARRS1 and 2MASS. Systematic differences between emission-based maps from missions like Planck (spacecraft) and stellar-reddening-based reconstructions from groups tied to Pan-STARRS1 produce discrepancies in high-column-density regions near Orion or the Galactic Center. Users must consider calibration choices made by research teams at institutions including Harvard University and Max Planck Society and validate against independent tracers such as molecular-line surveys from facilities like ALMA or IRAM.
dustmaps is distributed as a Python package interoperable with libraries and ecosystems provided by Python Software Foundation and scientific projects like Astropy, NumPy, and SciPy. It supports map retrieval from archives and mirrors maintained by organizations such as Space Telescope Science Institute and data centers like Centre de Données astronomiques de Strasbourg. Ancillary tools include interfaces for coordinate handling compatible with standards endorsed by the International Astronomical Union and plotting workflows using matplotlib and visualization suites common at institutions like Harvard & Smithsonian. Packaging and distribution leverage platforms associated with the Python Package Index and version control hosted on services used by research groups at GitHub, Inc..
Future development is likely to incorporate improved three-dimensional reconstructions informed by upcoming or successive data releases from Gaia and spectroscopic surveys like WEAVE and 4MOST, and to integrate thermal-emission constraints from future far-infrared missions. Continued collaboration with teams from Max Planck Society, Harvard University, and major survey consortia such as Pan-STARRS1 and Sloan Digital Sky Survey will refine spatial resolution and distance fidelity. Methodological advances from groups at universities like Princeton University and University of Cambridge in Bayesian tomography and machine learning approaches promise to reduce systematic uncertainties and better serve follow-on observations with facilities including James Webb Space Telescope and European Extremely Large Telescope.
Category:Astronomy software