We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique kn
own as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (Sep. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1 percent of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithms performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.
We present DES13S2cmm, the first spectroscopically-confirmed superluminous supernova (SLSN) from the Dark Energy Survey (DES). We briefly discuss the data and search algorithm used to find this event in the first year of DES operations, and outline t
he spectroscopic data obtained from the European Southern Observatory (ESO) Very Large Telescope to confirm its redshift (z = 0.663 +/- 0.001 based on the host-galaxy emission lines) and likely spectral type (type I). Using this redshift, we find M_U_peak = -21.05 +0.10 -0.09 for the peak, rest-frame U-band absolute magnitude, and find DES13S2cmm to be located in a faint, low metallicity (sub-solar), low stellar-mass host galaxy (log(M/M_sun) = 9.3 +/- 0.3); consistent with what is seen for other SLSNe-I. We compare the bolometric light curve of DES13S2cmm to fourteen similarly well-observed SLSNe-I in the literature and find it possesses one of the slowest declining tails (beyond +30 days rest frame past peak), and is the faintest at peak. Moreover, we find the bolometric light curves of all SLSNe-I studied herein possess a dispersion of only 0.2-0.3 magnitudes between +25 and +30 days after peak (rest frame) depending on redshift range studied; this could be important for standardising such supernovae, as is done with the more common type Ia. We fit the bolometric light curve of DES13S2cmm with two competing models for SLSNe-I - the radioactive decay of 56Ni, and a magnetar - and find that while the magnetar is formally a better fit, neither model provides a compelling match to the data. Although we are unable to conclusively differentiate between these two physical models for this particular SLSN-I, further DES observations of more SLSNe-I should break this degeneracy, especially if the light curves of SLSNe-I can be observed beyond 100 days in the rest frame of the supernova.
