Page:SATCON2 Algorithms Report.pdf/15

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 * For now, the Rubin Observatory team are using it on the difference between single exposures (point spread function matched warps) and a static sky model
 * The current implementation requires the full Rubin Observatory artillery (the Science Pipelines) to be run, so it is clearly not an off-the-shelf solution. “Derubinizing” the algorithm itself should be a manageable task, if the community decides it is desirable.
 * The CADC (Canadian Astronomy Data Center) Image Quality assessment process. In Teimoorinia et al. (2021), it was presented as a process for the detection of trailed images but the satellite problem is similar.
 * MaxiMask is a CNN-based (convolutional neural network) trail identifier
 * Desai et al, (2016) propose an algorithm that uses a deep co-added image of the same area of the sky as the exposure of interest
 * This may be too specific to sky survey-type observations to be of general use.
 * Gruen et al. (2014) have a publicly available, modified version of SWarp to remove artefacts, including satellite trails.
 * This algorithm also supposes numerous exposures of the same area are available.
 * StreakDet, a European Space Agency (ESA) software package. It was developed to find space debris streaks, e.g., for on-board processing on an optical payload. It is available under a weak-copy left license and is not open source.
 * Cosmic-CoNN (Xu et al,, 2021) is a CNN architecture for cosmic ray detection, though as they say it should be easily generalizable to satellite trails. Especially relevant is their proof of generalization to other instruments with minimum input data for retraining once the pretrained model has been trained on a large volume of data (from Las Cumbres Observatory in their case). As they say, “By expanding our dataset with more instruments from other facilities, we are confident to see an universal cosmic ray detection model that achieves better performance on unseen ground- based instruments without further training.”

Effort required. The effort needed to produce a TrailMask process will be dependent on the path selected. Adoption of the Legacy Survey of Space and Time (LSST) pipeline based trailmask or similar pipeline to a generic environment will likely require around 2 FTE. Simple codes that remove trails via image stacking require nearly no effort but are only effective for stacks.

3.1.4. Future algorithms: deep learning

Deep learning/AI methods for both detection and removal of satellite trails are being developed and may provide a highly effective approach to solving the problem of detection and removal of trails.

A deep learning/AI implementation would have the following user modes:

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