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Abstract

Solar system bodies such as comets and asteroids are known to eject material from their surface in the form of jets and plumes. Observations of these transient outbursts can offer insight into the inner workings and makeup of their originating body. However, the detection of and response to these events has thus far been manually controlled by ground operations, limiting the response time, due to the light time delay of ground communications. For distant bodies, the delay can exceed the duration of temporary events, making it impossible to respond with follow-up observations. To address this need, we developed a computer vision methodology for detecting plumes of the comet 67P/Churyumov–Gerasimenko from imagery acquired by the OSIRIS scientific camera system. While methods exist for the automatic detection of plumes on spherical and near-convex solar system bodies, this is the first work that addresses the case of highly irregularly shaped bodies such as 67P/Churyumov–Gerasimenko. Our work is divided into two distinct components: an image processing pipeline that refines a model-based estimate of the nucleus body, and an iterative plume detection algorithm that finds regions of local intensity maxima and joins plume segments across successively higher altitudes. Finally, we validate this method by comparing automatically labeled images to those labeled by hand, and find no significant differences in variability. This technique has utility in both ground-based analysis of plume sequences as well as onboard applications, such as isolating short sequences of high activity for priority downloading or triggering follow-up observations with additional instruments.