Galaxy Zoo Supernovae

TL;DR

Galaxy Zoo Supernovae leverages citizen scientists to efficiently identify supernova candidates from wide-field surveys, achieving accuracy comparable to expert scanners and aiding future transient detection efforts.

Contribution

This study introduces a novel citizen science approach for supernova detection that performs on par with professional scanners and enhances future transient search capabilities.

Findings

Achieved 93% agreement with professional PTF scanners.

Correctly identified all high signal-to-noise supernovae.

No false positives among the classified candidates.

Abstract

This paper presents the first results from a new citizen science project: Galaxy Zoo Supernovae. This proof of concept project uses members of the public to identify supernova candidates from the latest generation of wide-field imaging transient surveys. We describe the Galaxy Zoo Supernovae operations and scoring model, and demonstrate the effectiveness of this novel method using imaging data and transients from the Palomar Transient Factory (PTF). We examine the results collected over the period April-July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable - Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ~130 spectroscopically confirmed SNe that PTF located during the trial period (with no false positive identifications). Further analysis shows that only a small fraction of the lowest signal-to-noise SN detections (r > 19.5) are given low scores: Galaxy Zoo Supernovae correctly identifies all SNe with > 8{\sigma} detections in the PTF imaging data. The Galaxy Zoo Supernovae project has direct applicability to future transient searches such as the Large Synoptic Survey Telescope, by both rapidly identifying candidate transient events, and via the training and improvement of existing machine classifier algorithms.