The Processing subsystem in the Catalina Sky Survey (CSS) software suite is a streamlined pipeline designed to handle raw images captured by telescopes during asteroid and Near-Earth Object (NEO) surveys. This pipeline processes the images through multiple stages, such as calibrating for optical distortions, extracting star or asteroid positions, and converting these positions into standard astronomical coordinates. By automating the process, the system allows multiple operations to occur simultaneously, improving efficiency and speeding up data analysis. The CSS pipeline incorporates widely used astronomical techniques and public-domain software to ensure accuracy and reliability. While the processing module handles all necessary steps for image analysis, it does not include moving object detection, which is released separately. The system applies filters to identify known asteroids, NEOs, or satellites, allowing for accurate reporting of new potential discoveries. All detections that successfully emerge from the moving object detection pipeline are presented to an observer for final validation running under Control. This technology aligns with NASA's Open-Source Data Management Plan to support global research in tracking hazardous asteroids. Background: Processing large volumes of astronomical data to efficiently identify and track asteroids and Near-Earth Objects (NEOs) is challenging. Current solutions in NEO surveys often involve manual intervention or slower processing pipelines, which can delay the identification of new objects or overlook faint objects. These existing methods can be time-consuming, prone to errors, and may not allow for simultaneous handling of multiple operations, limiting their overall effectiveness in planetary defense. The CSS Processing subsystem improves on these issues by automating the image processing pipeline, enabling faster and more accurate data handling. Unlike traditional methods, it can perform multiple operations at once, such as calibrating for distortions and extracting object positions, speeding up the overall workflow. Its ability to filter out known objects and focus on potential new discoveries also reduces unnecessary data clutter. This approach streamlines the detection process and provides a more efficient way to analyze the vast amounts of data generated by NEO surveys. Applications:
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