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.
The Fermi Gamma-ray Burst Monitor (GBM) has detected over 1400 Gamma-Ray Bursts (GRBs) since it began science operations in July, 2008. We use a subset of over 300 GRBs localized by instruments such as Swift, the Fermi Large Area Telescope, INTEGRAL,
and MAXI, or through triangulations from the InterPlanetary Network (IPN), to analyze the accuracy of GBM GRB localizations. We find that the reported statistical uncertainties on GBM localizations, which can be as small as 1 degree, underestimate the distance of the GBM positions to the true GRB locations and we attribute this to systematic uncertainties. The distribution of systematic uncertainties is well represented (68% confidence level) by a 3.7 degree Gaussian with a non-Gaussian tail that contains about 10% of GBM-detected GRBs and extends to approximately 14 degrees. A more complex model suggests that there is a dependence of the systematic uncertainty on the position of the GRB in spacecraft coordinates, with GRBs in the quadrants on the Y-axis better localized than those on the X-axis.
We present systematic spectral analyses of GRBs detected with the Burst and Transient Source Experiment (BATSE) onboard the Compton Gamma-Ray Observatory (CGRO) during its entire nine years of operation. This catalog contains two types of spectra ext
racted from 2145 GRBs and fitted with five different spectral models resulting in a compendium of over 19000 spectra. The models were selected based on their empirical importance to the spectral shape of many GRBs, and the analysis performed was devised to be as thorough and objective as possible. We describe in detail our procedures and criteria for the analyses, and present the bulk results in the form of parameter distributions. This catalog should be considered an official product from the BATSE Science Team, and the data files containing the complete results are available from the High-Energy Astrophysics Science Archive Research Center (HEASARC).
We give necessary and sufficient conditions for the solvability of some semilinear elliptic boundary value problems involving the Laplace operator with linear and nonlinear highest order boundary conditions involving the Laplace-Beltrami operator.
