Discovering the Unexpected in Astronomical Survey Data

Abstract

Most major discoveries in astronomy are unplanned, and result from surveying the Universe in a new way, rather than by testing a hypothesis or conducting an investigation with planned outcomes. For example, of the 10 greatest discoveries made by the Hubble Space Telescope, only one was listed in its key science goals. So a telescope that merely achieves its stated science goals is not achieving its potential scientific productivity. Several next-generation astronomical survey telescopes are currently being designed and constructed that will significantly expand the volume of observational parameter space, and should in principle discover unexpected new phenomena and new types of object. However, the complexity of the telescopes and the large data volumes mean that these discoveries are unlikely to be found by chance. Therefore, it is necessary to plan explicitly for these unexpected discoveries in the design and construction of the telescope. Two types of discovery are recognised: unexpected objects, and unexpected phenomena. This paper argues that next-generation astronomical surveys require an explicit process for detecting the unexpected, and proposes an implementation of this process. This implementation addresses both types of discovery, and relies heavily on machine-learning techniques, and also on theory-based simulations that encapsulate our current understanding of the Universe to compare with the data.

References in corpus (30)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• Simulating the joint evolution of quasars, galaxies and their large-scale distribution
• A bright millisecond radio burst of extragalactic origin
• The Hubble Deep Field: Observations, Data Reduction, and Galaxy Photometry
• EMU: Evolutionary Map of the Universe
• Radio sources in the 6dFGS: Local luminosity functions at 1.4 GHz for star-forming galaxies and radio-loud AGN
• Resolving the Radio Source Background: Deeper Understanding Through Confusion
• Deep ATLAS radio observations of the CDFS-SWIRE field
• The VLA Survey of the Chandra Deep Field South. V. Evolution and Luminosity Functions of sub-mJy radio sources and the issue of radio emission in radio-quiet AGN
• The weirdest SDSS galaxies: results from an outlier detection algorithm
• The Commensal Real-time ASKAP Fast Transients (CRAFT) survey
• VAST: An ASKAP Survey for Variables and Slow Transients
• Radio Continuum Surveys with Square Kilometre Array Pathfinders
• Unsupervised self-organised mapping: a versatile empirical tool for object selection, classification and redshift estimation in large surveys
• ATLAS - I. Third Release of 1.4 GHz Mosaics and Component Catalogues
• The Exploration of the Unknown
• The Theoretical Astrophysical Observatory: Cloud-Based Mock Galaxy Catalogues
• The ASKAP/EMU Source Finding Data Challenge
• The Australia Telescope Large Area Survey: Spectroscopic Catalogue and Radio Luminosity Functions
• ATLAS 1.4 GHz Data Release 2 -- II. Properties of the faint polarized sky
• Infrared-Faint Radio Sources: A New Population of High-redshift Radio Galaxies
• Deep 610-MHz Giant Metrewave Radio Telescope observations of the Spitzer extragalactic First Look Survey field - III. The radio properties of Infrared-Faint Radio Sources
• Radio Galaxy Zoo: discovery of a poor cluster through a giant wide-angle tail radio galaxy
• Automated detection of extended sources in radio maps: progress from the SCORPIO survey
• Radio galaxies in ZFOURGE/NMBS: no difference in the properties of massive galaxies with and without radio-AGN out to z = 2.25
• Feature Detection in Radio Astronomy using the Circle Hough Transform
• Serendipity in Astronomy
• The ATLAS-SPT Radio Survey of Cluster Galaxies
• Detecting Diffuse Sources in Astronomical Images

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• Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
• Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification
• The future of astronomy with small satellites
• Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
• Identifying complex sources in large astronomical data using a coarse-grained complexity measure
• Identifying anomalous radio sources in the EMU Pilot Survey using a complexity-based approach
• One of Everything: The Breakthrough Listen Exotica Catalog
• Future Exoplanet Research: Science Questions and How to Address Them
• Astroinformatics Challenges from Next-generation Radio Continuum Surveys
• SPARKESX: Single-dish PARKES data sets for finding the uneXpected -- A data challenge
• Pushing the Technical Frontier: From Overwhelmingly Large Data Sets to Machine Learning
• Closing the loop: surveying PIs who have not published their data
• Understanding the human in the design of cyber-human discovery systems for data-driven astronomy