  Autonomous digital sky surveys such as Pan-STARRS have the ability to image a
very large number of galactic and extra-galactic objects, and the large and
complex nature of the image data reinforces the use of automation. Here we
describe the design and implementation of a data analysis process for automatic
broad morphology annotation of galaxies, and applied it to the data of
Pan-STARRS DR1. The process is based on filters followed by a two-step
convolutional neural network (CNN) classification. Training samples are
generated by using an augmented and balanced set of manually classified
galaxies. Results are evaluated for accuracy by comparison to the annotation of
Pan-STARRS included in a previous broad morphology catalog of SDSS galaxies.
Our analysis shows that a CNN combined with several filters is an effective
approach for annotating the galaxies and removing unclean images. The catalog
contains morphology labels for 1,662,190 galaxies with ~95% accuracy. The
accuracy can be further improved by selecting labels above certain confidence
thresholds. The catalog is publicly available.
