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.
