In this review, we explore the historical development and future prospects of artificial
intelligence (AI) and deep learning in astronomy. We trace the evolution of connectionism in …

We present detailed morphology measurements for 8.67 million galaxies in the DESI Legacy
Imaging Surveys (DECaLS, MzLS, and BASS, plus DES). These are automated …

Artificial intelligence methods show great promise in increasing the quality and speed of
work with large astronomical datasets, but the high complexity of these methods leads to the …

In this work, we apply self-supervised learning with instance differentiation to learn a robust,
multipurpose representation for image analysis of resolved extragalactic continuum images …

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 …

We present AstroCLIP, a strategy to facilitate the construction of astronomical foundation
models that bridge the gap between diverse observational modalities. We demonstrate that …

We propose\textit {masked particle modeling}(MPM) as a self-supervised method for
learning generic, transferable, and reusable representations on unordered sets of inputs for …

Citizen science is gaining popularity as a valuable tool for labelling large collections of
astronomical images by the general public. This is often achieved at the cost of poorer …

Aims. Various galaxy merger detection methods have been applied to diverse datasets.
However, it is difficult to understand how they compare. Our aim is to benchmark the relative …

Self-Supervised Learning (SSL) is at the core of training modern large machine learning
models, providing a scheme for learning powerful representations that can be used in a …