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| AstroDrizzle | |
|---|---|
| Name | AstroDrizzle |
| Developer | Space Telescope Science Institute |
| Released | 2006 |
| Latest release | 2012 |
| Programming language | Python, C |
| Platform | Unix, Linux, macOS |
| Genre | Astronomical image processing |
AstroDrizzle AstroDrizzle is an astronomical image processing package used for combining and correcting exposures from spaceborne and ground-based observatories. It operates within a pipeline developed by the Space Telescope Science Institute and interfaces with software and missions associated with Hubble Space Telescope, James Webb Space Telescope, Chandra X-ray Observatory, Keck Observatory, and European Southern Observatory. The package is widely used in archival reduction, survey projects, and instrument teams affiliated with NASA, ESA, Space Telescope Science Institute, STScI, and university consortia.
AstroDrizzle is a descendant of the Drizzle algorithm originally employed for Hubble Space Telescope imaging campaigns such as Hubble Deep Field, Hubble Ultra Deep Field, and CANDELS. It is distributed as part of the DrizzlePac toolkit maintained by the Space Telescope Science Institute and integrates with ancillary tools like TweakReg, AstroConda, and PyRAF. AstroDrizzle automates key tasks including geometric distortion correction, cosmic-ray rejection, subpixel resampling, and sky subtraction for instruments including Wide Field Camera 3, Advanced Camera for Surveys, and legacy Wide Field Planetary Camera 2. Its design supports pipelines for missions and projects associated with NASA Goddard Space Flight Center, European Space Agency, and observatory archives such as the Mikulski Archive for Space Telescopes.
The core Drizzle algorithm was developed during the 1990s to address undersampled point-spread functions in Hubble Space Telescope observations, with early applications in programs led by teams including Robert Hook, John Bahcall, and survey consortia behind Hubble Deep Field. Subsequent evolution produced AstroDrizzle as part of mission-specific toolchains at Space Telescope Science Institute under funding and oversight from NASA and partnerships with European Space Agency investigators. Major updates coincided with instrument milestones such as the installation of Wide Field Planetary Camera 2 and later instruments like Advanced Camera for Surveys and Wide Field Camera 3, and with cross-mission interoperability efforts involving James Webb Space Telescope teams, Chandra X-ray Observatory analysts, and archival initiatives at institutions such as STScI and MAST.
AstroDrizzle implements an advanced resampling approach based on the original Drizzle technique, incorporating variable pixel footprint kernels, output pixel fraction parameters, and weight maps derived from instrument characteristics. The pipeline performs distortion correction using instrument models derived by teams at Space Telescope Science Institute and calibration consortia linked to STScI Calibration Group, Instrument Science Teams, and observatory calibration databases like CALDB. Cosmic-ray detection and rejection routines borrow techniques developed for CCD and MCP detectors used on Hubble Space Telescope and Chandra X-ray Observatory, while background matching and sky subtraction strategies reflect methodologies adopted in large surveys such as CANDELS, COSMOS, and GOODS. The software interoperates with libraries and languages used by mission software teams, including Python (programming language), NumPy, SciPy, and legacy interfaces such as IRAF and PyRAF.
Typical AstroDrizzle workflows begin with association table creation for exposures obtained by instruments like Wide Field Camera 3 or Advanced Camera for Surveys, followed by image registration using tools such as TweakReg and astrometric catalogs from Gaia, 2MASS, or mission-specific reference frames. Users configure parameters for drizzle pixel fraction, output pixel scale, and cosmic-ray rejection thresholds, and then execute multi-stage processing producing single-drizzled, median-combined, and final cosmic-ray-cleaned products. Pipelines incorporating AstroDrizzle are used by survey teams behind projects like PHAT, LEGUS, and Frontier Fields, as well as instrument teams at Space Telescope Science Institute and observatory data centers such as MAST.
AstroDrizzle consumes calibrated exposures in FITS format prepared by mission calibration pipelines (e.g., CALACS, CALWF3 for Hubble Space Telescope instruments) and uses header metadata standardized by observatory conventions and the Flexible Image Transport System. Output products are FITS files including weight maps, context images, and drizzle-combined science extensions compatible with analysis tools used by teams working on Hubble Space Telescope archival science, James Webb Space Telescope follow-ups, and multiwavelength campaigns integrating data from Spitzer Space Telescope, Chandra X-ray Observatory, and ground facilities such as Keck Observatory and Very Large Telescope.
AstroDrizzle performs robustly on datasets from well-characterized instruments like Wide Field Camera 3 and Advanced Camera for Surveys, but its performance depends on accurate distortion solutions and reliable cosmic-ray rejection provided by calibration teams at STScI Calibration Group. Limitations include sensitivity to input pointing errors when astrometric reference catalogs such as Gaia are not used, challenges with extremely crowded fields encountered in programs like PHAT and transient-rich fields monitored by Zwicky Transient Facility, and computational resource demands for large mosaics produced by survey teams like CANDELS and COSMOS. Ongoing development efforts coordinated with Space Telescope Science Institute and community instrument teams address scalability and compatibility with pipelines used by James Webb Space Telescope and future observatories.
AstroDrizzle has been instrumental in producing high-fidelity mosaics and deep combined images for legacy programs such as Hubble Deep Field, Hubble Ultra Deep Field, CANDELS, GOODS, and Frontier Fields. It is routinely applied in archival reprocessing projects at MAST, cross-calibration studies involving Chandra X-ray Observatory and Spitzer Space Telescope teams, and in support of follow-up observations from facilities like Keck Observatory, Very Large Telescope, and Subaru Telescope. Instrument teams at Space Telescope Science Institute and survey consortia continue to employ AstroDrizzle in calibration, science verification, and production of science-ready data delivered to community archives.
Category:Astronomical image processing software