A catalog of broad morphology of Pan-STARRS galaxies based on deep learning

TL;DR

This paper presents an automated deep learning approach for classifying galaxy morphologies in Pan-STARRS data, achieving high accuracy and creating a large, publicly available catalog of galaxy types.

Contribution

It introduces a CNN-based method with filters for broad galaxy morphology annotation, applied to Pan-STARRS DR1, with improved accuracy over previous catalogs.

Findings

Achieved ~95% accuracy in galaxy morphology classification.

Generated a catalog of 1.66 million galaxies with morphology labels.

Demonstrated effectiveness of CNNs combined with filtering for image classification.

Abstract

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.