Page:SATCON2 Algorithms Report.pdf/11



Here we discuss one by one the individual software algorithms that we see as responding to the SATCON1 recommendations. It is likely beyond the scope of this working group to choose or down-select a particular approach but we can provide some guidance on how such a selection might be made.

Support development of a software application available to the general astronomy community to identify, model, subtract and mask satellite trails in images on the basis of user-supplied parameters.

As noted in Section 1, both programmatic and web-based interfaces should be provided. The latter will be of particular use to the hobbyist community.

3.1.1. Inputs and outputs

Required inputs:  Image(s) where trails should be identified Image parameters (field of view, pixel size, flux calibration; would usually be in the image’s header) Trail search parameters (width ranges, signal-to-noise ratio etc.; should come with reasonable default values if derivable from input b) 

Additional, optional inputs (depending on the mode TrailMask is run in):  Time and pointing of observation (for seeded mode) Prior information on where trails are expected to be present (for seeded mode) Simulated satellite traces planted on real images and also the images without the traces as the training set for deep learning models Real satellite traces in images — coming from the test data suite and elsewhere.</li> <li>Images from the same region as g) without the traces as an alternative training set for deep learning models</li> </ol> Rh