Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics

Abstract

Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large Synoptic Survey Telescope and the Square Kilometer Array are planned to become operational in this decade and the next, and will increase the data volume by many orders of magnitude. The increased spatial, temporal and spectral resolution afford a powerful magnifying lens on the physical processes that underlie the data but, at the same time, generate unprecedented complexity hard to exploit for knowledge extraction. It is therefore imperative to develop machine intelligence, machine learning (ML) in particular, suitable for processing the amount and variety of astronomical data that will be collected, and capable of answering scientific questions based on the data. Astronomical data exhibit the usual challenges associated with 'big data' such as immense volumes, high dimensionality, missing or highly distorted observations. In addition, astronomical data can exhibit large continuous observational gaps, very low signal-to-noise ratio and the need to distinguish between true missing data and non-detections due to upper limits). There are strict laws of physics behind the data production which can be assimilated into ML mechanisms to improve over general off-the-shelf state-of-the-art methods. Significant progress in the face of these challenges can be achieved only via the new discipline of Astroinformatics: a symbiosis of diverse disciplines, such as ML, probabilistic modeling, astronomy and astrophysics, statistics, distributed computing and natural computation. This editorial summarizes the contents of a soon to appear Focus Issue of the PASP on Machine Learning in Astronomy and Astrophysics (with contributions by 69 authors representing 15 countries, from 6 continents).

arXiv comment11 pages

References in corpus (19)

• The Sloan Digital Sky Survey: Technical Summary
• Methods for Interpreting and Understanding Deep Neural Networks
• Collaborative Nested Sampling: Big Data vs. complex physical models
• Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection
• METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts
• Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
• Deep Learning for Image Sequence Classification of Astronomical Events
• Return of the features. Efficient feature selection and interpretation for photometric redshifts
• Star Formation Rates for photometric samples of galaxies using machine learning methods
• Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
• Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes
• The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
• A Comparison of Photometric Redshift Techniques for Large Radio Surveys
• Multiband galaxy morphologies for CLASH: a convolutional neural network transferred from CANDELS
• Preliminary results of using k-Nearest Neighbours Regression to estimate the redshift of radio selected datasets
• Sacrificing information for the greater good: how to select photometric bands for optimal accuracy
• Unsupervised Classification of Galaxies. I. ICA feature selection
• Identifying complex sources in large astronomical data using a coarse-grained complexity measure
• Kernel regression estimates of time delays between gravitationally lensed fluxes

Cited by in corpus (10)

• 2022 Review of Data-Driven Plasma Science
• Evaluating the classification of Fermi BCUs from the 4FGL Catalog Using Machine Learning
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
• 3D Detection and Characterisation of ALMA Sources through Deep Learning
• Searching for Possible Exoplanet Transits from BRITE Data through a Machine Learning Technique
• Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
• SHAPE. I. A SOM-SED hybrid approach for efficient galaxy parameter estimation leveraging JWST
• ULISSE: Determination of star-formation rate and stellar mass based on the one-shot galaxy imaging technique
• Very-Long Baseline Interferometry Imaging with Closure Invariants using Conditional Image Diffusion
• Virtual Realities: Is there only one advanced image display that astronomers need?