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.

Extragalactic radio continuum surveys and the transformation of radio astronomy

“…Instead, most science will be extracted from the large survey datasets by querying the data with specific questions, resulting in specific answers. To discover the unexpected, we need to develop algorithms [41] that can mine the data for the unexpected, such as that developed by [75] to identify "weird" galaxies in SDSS data by looking for abnormal spectra.…”

Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images

“…Large radio continuum surveys have played a key role in our understanding of the evolution of galax-ies (Norris 2017a). Exceptionally large surveys such as Low-frequency Array (LOFAR) Two-metre Sky Survey (LOTSS) (Shimwell et al 2017) and the Evolution-arXiv:1906.02864v1 [astro-ph.IM] 7 Jun 2019 ary Map of the Universe (Norris et al 2011, EMU) are expected to detect 30 million and 70 million radio sources respectively.…”

“…However, this supervised training is not always suitable in outlier detection and separating source complexity as it requires a more complete knowledge of all potential classes of new unseen data. Given that most of the major discoveries in astronomy have been unplanned (Norris 2017b), this is a major shortcoming.…”

Future Exoplanet Research: Science Questions and How to Address Them

“…In this chapter we will develop three aspects: new science questions, new objects, new instrumentation. We will also go from safe anticipation to more risky speculations, bearing in mind that "Most major discoveries in astronomy are unplanned" (Norris 2016).…”