Real-time processing pipeline for automatic streak detection in astronomical images implemented in a multi-GPU system

Abstract Detecting and tracking objects in low Earth orbit is an increasingly important task. Telescope observations contribute to its accomplishment, and telescope imagers produce a large amount of data for this task. Thus, it is convenient to use fast computer-aided processes to analyze it. Telescopes tracking at the sidereal rate usually detect these objects in their imagers as streaks, their lengths depending on the exposure time and the slant range to the object. We have developed a processing pipeline to automatically detect streaks in astronomical images in real time (i.e., faster than the images are produced) by a graphics processing unit parallel processing system. After the detection stage, streak photometric information is obtained, and object candidate identification is provided through matches with a two-line element set database. The system has been tested on a large set of images, consisting of two hours of observation time, from the Tomo-e Gozen camera of the 105 cm Schmidt telescope at Kiso Observatory in Japan. Streaks were automatically detected in approximately 0.5% of the images. The process detected streaks down to a minimum apparent magnitude of +11.3 and matched the streaks with objects from the space-track catalog in 78% of the cases. We believe that this processing pipeline can be instrumental in detecting new objects and tracking existing ones when processing speed is important, for instance, when a short handover time is required between follow-up observation stations, or when there is a large number of images to process. This study will contribute to consolidating optical observations as an effective way to control and alleviate the space debris problem.