Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Abstract

Machine learning (automated processes that learn by example in order to classify, predict, discover or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks (artificial, deep, and convolutional) are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally-lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insight. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses or becomes established.

40 pages, accepted for publication in WIREs Data Mining and Knowledge Discovery (9 November 2019)

References in corpus (58)

• The Gaia mission
• Methods for Interpreting and Understanding Deep Neural Networks
• Gaia Data Release 1. Summary of the astrometric, photometric, and survey properties
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Science with ASKAP - the Australian Square Kilometre Array Pathfinder
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Science With The Australian Square Kilometre Array Pathfinder
• The Fundamental Properties of Galaxies and a New Galaxy Classification System
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Galaxy Interactions Trigger Rapid Black Hole Growth: an unprecedented view from the Hyper Suprime-Cam Survey
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers
• Classifying Radio Galaxies with Convolutional Neural Network
• Machine Learning for the Zwicky Transient Facility
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• Searching for Exoplanets Using Artificial Intelligence
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm
• A recurrent neural network for classification of unevenly sampled variable stars
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources: Application in the ZTF Real-Time Pipeline
• Initial Data Release from the INT Photometric H-alpha Survey of the Northern Galactic Plane (IPHAS)
• An automatic taxonomy of galaxy morphology using unsupervised machine learning
• VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning
• METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
• The-wiZZ: Clustering redshift estimation for everyone
• MeerKAT Key Project Science, Specifications, and Proposals
• Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
• Feature importance for machine learning redshifts applied to SDSS galaxies
• Data Mining for Gravitationally Lensed Quasars
• Improved $\gamma$/hadron separation for the detection of faint gamma-ray sources using boosted decision trees
• Machine learning search for variable stars
• Discovering the Unexpected in Astronomical Survey Data
• IPHAS discoveries of young stars towards Cyg OB2 and its southern periphery
• Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
• Transit Shapes and Self Organising Maps as a Tool for Ranking Planetary Candidates: Application to Kepler and K2
• Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys
• The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection
• A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data
• Evaluating the optical classification of Fermi BCUs using machine learning
• Photometric redshifts with Quasi Newton Algorithm (MLPQNA). Results in the PHAT1 contest
• Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters
• The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility
• Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data
• Unsupervised Classification of Variable Stars
• A Machine Learning Artificial Neural Network Calibration of the Strong-Line Oxygen Abundance
• An artificial neural network to discover Hypervelocity stars: Candidates in Gaia DR1/TGAS
• Effect of training characteristics on object classification: an application using Boosted Decision Trees
• Characterization of Near-Earth Asteroids using KMTNet-SAAO
• A Bayesian Framework for Cosmic String Searches in CMB Maps
• Inferring physical properties of galaxies from their emission line spectra
• Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
• Automatic Survey-Invariant Variable Star Classification
• DAMEWARE: A web cyberinfrastructure for astrophysical data mining
• Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission
• Applications of Machine-Learning Algorithms for Infrared Colour Selection of Galactic Wolf-Rayet Stars
• Detecting unresolved binary stars in Euclid VIS images

Cited by in corpus (40)

• Painting a portrait of the Galactic disc with its stellar clusters
• "Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?
• A machine learning approach to mapping baryons onto dark matter haloes using the EAGLE and C-EAGLE simulations
• On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2
• Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost
• Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes
• Photometric redshifts with machine learning, lights and shadows on a complex data science use case
• Machine Learning applied to asteroid dynamics
• XLB: A differentiable massively parallel lattice Boltzmann library in Python
• Classification of Planetary Nebulae through Deep Transfer Learning
• PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
• Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy Formation
• Interpreting automatic AGN classifiers with saliency maps
• Recovering Cosmic Microwave Background Polarization Signals with Machine Learning
• Mapping the X-ray variability of GRS1915+105 with machine learning
• Neural network-based emulation of interstellar medium models
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
• A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
• A Census of Protostellar Outflows in Nearby Molecular Clouds
• Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• Identifying Potential Exomoon Signals with Convolutional Neural Networks
• CzSL: Learning from citizen science, experts and unlabelled data in astronomical image classification
• The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques
• Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
• Lensing reconstruction from the cosmic microwave background polarization with machine learning
• A morphological segmentation approach to determining bar lengths
• How to set up your first machine learning project in astronomy
• Galaxy pairs in The Three Hundred simulations II: studying bound ones and identifying them via machine learning
• StarUnLink: identifying and mitigating signals from communications satellites in stellar spectral surveys
• Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction
• Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment
• Machine learning for exoplanet detection in high-contrast spectroscopy Combining cross correlation maps and deep learning on medium-resolution integral-field spectra
• Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification
• Gamma-ray burst light curve reconstruction with predictive models
• ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
• A gradient boosting and broadband approach to finding Lyman-α emitting galaxies beyond narrowband surveys
• A machine learning approach to estimate mid-infrared fluxes from WISE data
• Prediction of Individual Halo Concentrations Across Cosmic Time Using Neural Networks
• Bar Properties and Star-by-Star Bar Membership via Action Conservation