Zoobot: Adaptable Deep Learning Models for Galaxy Morphology

Abstract

Zoobot is a Python package for measuring the detailed appearance of galaxies in telescope images using deep learning. Zoobot is aimed at astronomers who want to solve a galaxy image task such as finding merging galaxies or counting spiral arms. Astronomers can use Zoobot to adapt (finetune) pretrained deep learning models to solve their task. These finetuned models perform better and require far fewer new labels than training from scratch (Walmsley, Slijepcevic, et al., 2022). The models included with Zoobot are pretrained on up to 92 million responses from Galaxy Zoo volunteers. Each volunteer answers a series of tasks describing the detailed appearance of each galaxy. Zoobot's models are trained to answer all of these diverse tasks simultaneously. The models can then be adapted to new related tasks. Zoobot provides a high-level API and guided workflow for carrying out the finetuning process. The API abstracts away engineering details such as efficiently loading astronomical images, multi-GPU training, iteratively finetuning deeper model layers, and so forth. Behind the scenes, these steps are implemented via either PyTorch or TensorFlow, according to the user's choice. Zoobot is therefore accessible to astronomers with no previous experience in deep learning. For advanced users, Zoobot also includes the code to replicate and extend our pretrained models. This is used routinely at Galaxy Zoo to scale up galaxy measurement catalogs (Walmsley, Lintott, et al., 2022) and to prioritise the galaxies shown to volunteers for labelling. Zoobot models have been applied to measure galaxy appearance in SDSS (Walmsley et al., 2020), Hubble, HSC, and DESI, and are included in the data pipeline of upcoming space telescope Euclid (Laureijs et al., 2011). We hope that Zoobot will help empower astronomers to apply deep learning to answer their own science questions.