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

arXiv: https://arxiv.org/abs/2010.06073 · HTML: https://mlsanity.com/paper/2010.06073/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 5 of 5)

- [Systematic biases when using deep neural networks for annotating large catalogs of astronomical images](https://mlsanity.com/paper/2201.03131/) (2022)
- [Automatic identification of outliers in Hubble Space Telescope galaxy images](https://mlsanity.com/paper/2101.02623/) (2021)
- [Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning](https://mlsanity.com/paper/2305.01720/) (2023)
- [Galaxy image simplification using Generative AI](https://mlsanity.com/paper/2507.11692/) (2025)
- [Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies](https://mlsanity.com/paper/2405.02366/) (2024)

## Abstract (arXiv)

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.