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 …

Matter evolved under the influence of gravity from minuscule density fluctuations.
Nonperturbative structure formed hierarchically over all scales and developed non …

Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. So far, these neural …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the
task of distinguishing between merging and non-merging galaxies in simulated images, and …

We present a deep machine learning (ML)–based technique for accurately determining σ 8
and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos …

In this work, we explore the possibility of using artificial neural networks to impose
constraints on teleparallel gravity and its f (T) extensions. We use the available Hubble …

Understanding the impact of halo properties beyond halo mass on the clustering of galaxies
(namely galaxy assembly bias) remains a challenge for contemporary models of galaxy …

The presence of dust in spiral galaxies affects the ability of photometric decompositions to
retrieve the parameters of their main structural components. For galaxies in an edge-on …

The applications of artificial neural networks in the cosmological field have shone
successfully during the past decade, this is due to their great ability of modeling large …