Machine Learning in Astronomy: a practical overview

Abstract

Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic tools to mine datasets and extract novel information from them. In recent years, machine learning algorithms have become increasingly popular among astronomers, and are now used for a wide variety of tasks. In light of these developments, and the promise and challenges associated with them, the IAC Winter School 2018 focused on big data in Astronomy, with a particular emphasis on machine learning and deep learning techniques. This document summarizes the topics of supervised and unsupervised learning algorithms presented during the school, and provides practical information on the application of such tools to astronomical datasets. In this document I cover basic topics in supervised machine learning, including selection and preprocessing of the input dataset, evaluation methods, and three popular supervised learning algorithms, Support Vector Machines, Random Forests, and shallow Artificial Neural Networks. My main focus is on unsupervised machine learning algorithms, that are used to perform cluster analysis, dimensionality reduction, visualization, and outlier detection. Unsupervised learning algorithms are of particular importance to scientific research, since they can be used to extract new knowledge from existing datasets, and can facilitate new discoveries.

37 pages, 18 figures

References in corpus (12)

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• The DESI Experiment, a whitepaper for Snowmass 2013
• Photometric Supernova Classification With Machine Learning
• Deep-Learnt Classification of Light Curves
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets
• Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys
• Using the Properties of Broad Absorption Line Quasars to Illuminate Quasar Structure
• Teaching a machine to see: unsupervised image segmentation and categorisation using growing neural gas and hierarchical clustering
• An approach to the analysis of SDSS spectroscopic outliers based on Self-Organizing Maps
• Autoencoding Time Series for Visualisation
• Model-Coupled Autoencoder for Time Series Visualisation

Cited by in corpus (70)

• Painting a portrait of the Galactic disc with its stellar clusters
• Catalogue of new Herbig Ae/Be and classical Be stars. A machine learning approach to Gaia DR2
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• Horizons: Nuclear Astrophysics in the 2020s and Beyond
• Learning the Relationship between Galaxies Spectra and their Star Formation Histories using Convolutional Neural Networks and Cosmological Simulations
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• A review of unsupervised learning in astronomy
• Machine learning classification of CHIME fast radio bursts -- I. Supervised methods
• Galaxy cluster mass estimation with deep learning and hydrodynamical simulations
• Multi-phase outflows in post starburst E+A galaxies -- I. General sample properties and the prevalence of obscured starbursts
• Predicting star formation properties of galaxies using deep learning
• {\sc mirkwood:} Fast and Accurate SED Modeling Using Machine Learning
• Galaxy Morphology Classification using Neural Ordinary Differential Equations
• Anomaly Detection in Gravitational Waves data using Convolutional AutoEncoders
• Improving the reliability of photometric redshift with machine learning
• The evolution of barred galaxies in the EAGLE simulations
• Photometric redshifts with machine learning, lights and shadows on a complex data science use case
• Machine Learning applied to asteroid dynamics
• SUPPNet: Neural network for stellar spectrum normalisation
• Hierarchical Clustering in Astronomy
• Galaxy classification: deep learning on the OTELO and COSMOS databases
• A Machine-Learning-Based Direction-of-Origin Filter for the Identification of Radio Frequency Interference in the Search for Technosignatures
• Extracting the main trend in a dataset: the Sequencer algorithm
• Classification of Fermi-LAT sources with deep learning using energy and time spectra
• A semi-supervised Machine Learning search for never-seen Gravitational-Wave sources
• The Probabilistic Random Forest applied to the selection of quasar candidates in the QUBRICS Survey
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning
• A statistical study of the optical spectral variability in gamma-ray blazars
• Beyond the Local Volume. I. Surface Densities of Ultracool Dwarfs in Deep HST/WFC3 Parallel Fields
• Searching for AGN and Pulsar Candidates in 4FGL Unassociated Sources Using Machine Learning
• PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
• Photometric identification of compact galaxies, stars and quasars using multiple neural networks
• Shaping physical properties of galaxy subtypes in the VIPERS survey: environment matters
• B/PS bulges in DESI Legacy edge-on galaxies I: Sample building
• Density Based Outlier Scoring on Kepler Data
• Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning
• Machine Learning Approach to Integral Field Unit Spectroscopy Observations: I. HII Region Kinematics
• Exploring X-ray variability with unsupervised machine learning I. Self-organizing maps applied to XMM-Newton data
• Classifications of Fermi-LAT unassociated sources in multiple machine learning methods
• FERIA: Flat Envelope Model with Rotation and Infall under Angular Momentum Conservation
• Constraining stellar population parameters from narrow band photometric surveys using convolutional neural networks
• Analyzing the Galactic pulsar distribution with machine learning
• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• 3D Detection and Characterisation of ALMA Sources through Deep Learning
• Field/Isolated lenticular galaxies with high $S_N$ values: the case of NGC 4546 and its globular cluster system
• Astroinformatics based search for globular clusters in the Fornax Deep Survey
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• Unveiling the quasar main sequence: illuminating the complexity of active galactic nuclei and their evolution
• Automated classification of eclipsing binary systems in the VVV Survey
• Galaxy Spectra Networks (GaSNet). III. Generative pre-trained network for spectrum reconstruction, redshift estimate and anomaly detection
• Applied Machine-Learning Models to Identify Spectral Sub-Types of M Dwarfs from Photometric Surveys
• Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks
• Predicting bulge to total luminosity ratio of galaxies using deep learning
• Minimization of ion micromotion with artificial neural network
• Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit
• A morphological segmentation approach to determining bar lengths
• A catalogue of complex radio sources in the Rapid ASKAP Continuum Survey created using a Self-Organising Map
• C2-GaMe: Classification of Cluster Galaxy Membership with Machine Learning
• Real-bogus scores for active anomaly detection
• Search for glitches of gamma-ray pulsars with deep learning
• Clusternets: A deep learning approach to probe clustering dark energy
• Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED
• Galactic Component Mapping of Galaxy UGC 2885 by Machine Learning Classification
• Construction of a far ultraviolet all sky map from an incomplete survey: Application of a deep learning algorithm
• From Photometric Redshifts to Improved Weather Forecasts: machine learning and proper scoring rules as a basis for interdisciplinary work
• In search of the weirdest galaxies in the Universe
• Virtual Realities: Is there only one advanced image display that astronomers need?
• Searching for outliers in the Chandra Source Catalog