2017/07/17 by Darryl E. Wright, Darryl Wright, Chris J. Lintott +37 · 51 citations
Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astronomy #Astrophysics and Cosmic Phenomena #Computer science #Convolutional neural network #Data mining #Data science #Gamma-ray bursts and supernovae #Identification (biology) #Large Synoptic Survey Telescope #Machine learning #Noise (video) #Physics #Software deployment #Software engineering #Stellar, planetary, and galactic studies #Supernova #Telescope #Transient (computer programming) #astro-ph.IM
paper · pdf · doi:10.1093/mnras/stx1812
published in Monthly Notices of the Royal Astronomical Society 472(2), 1315-1323 (Oxford University Press) · 10 pages, 9 figures, submitted to MNRAS
arxiv created 2017/07/17 · openalex publication_date 2017/07/18 · arxiv updated 2017/09/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.