We trained the model with the 1,613 satellite trails tagged by the volunteers on Hubble Asteroid Hunter and split the sample into 70% training, 15% validation and 15% test sets for AutoML to optimize the hyperparameters of the model and evaluate its performance. The model achieves a precision of 91.7% and recall of 84.4% on the test set, at a 50% classification confidence threshold.
We find a fraction of 8.9 ± 1.1% composite ACS/WFC and 5.8 ± 0.7% composite WFC3/UVIS images crossed by satellites. On average, 3.2 individual exposures were combined to create the composite images. This corresponds to a fraction of HST images with satellites of 2.8 ± 0.3% for the individual ACS/WFC and 1.8 ± 0.2% for individual WFC3/UVIS exposures, an average of 2.4 ± 0.3% for the two instruments.
NatureAstronomy Ask elon Musk about his so many starlinks .