The Difference Imaging Pipeline for the Transient Search in the Dark Energy Survey

Abstract

We describe the operation and performance of the difference imaging pipeline (DiffImg) used to detect transients in deep images from the Dark Energy Survey Supernova program (DES-SN) in its first observing season from 2013 August through 2014 February. DES-SN is a search for transients in which ten 3 deg 2 fields are repeatedly observed in the g, r, i, z passbands with a cadence of about 1 week. The observing strategy has been optimized to measure high-quality light curves and redshifts for thousands of Type Ia supernovae (SNe Ia) with the goal of measuring dark energy parameters. The essential DiffImg functions are to align each search image to a deep reference image, do a pixel-by-pixel subtraction, and then examine the subtracted image for significant positive detections of point-source objects. The vast majority of detections are subtraction artifacts, but after selection requirements and image filtering with an automated scanning program, there are ∼130 detections per deg 2 per observation in each band, of which only ∼25% are artifacts. Of the ∼7500 transients discovered by DES-SN in its first observing season, each requiring a detection on at least two separate nights, Monte Carlo (MC) simulations predict that 27% are expected to be SNe Ia or core-collapse SNe. Another ∼30% of the transients are artifacts in

The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. I. Discovery of the Optical Counterpart Using the Dark Energy Camera

“…We process all images with the DES single-epoch processing (Drlica-Wagner et al 2017; E. Morganson et al 2017, in preparation, and references therein) and difference imaging (diffimg) pipelines (Kessler et al 2015). The diffimg software works by comparing search images and one or more reference images (templates) obtained before or after the search images.…”

“…This process results in 1500 transient candidates with magnitudes between 15.5 and 20.5. A candidate is defined as a detection meeting diffimg quality requirements (see Table 3 of Kessler et al 2015) on at least two search exposures. The magnitude cutoff of this analysis is limited by the depth of the template images: i=21.2 and z=20.5, to be compared with the depth of the search images: i=22.0 and z=21.3.…”

Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-collapse Supernova Contamination

“…The intrinsic dispersion, s int , is defined as the value added in quadrature to the SN Ia distance modulus uncertainty such that the Hubble diagram reduced c 2 is equal to 1 (Guy et al 2007). Differences in SN Ia intrinsic dispersion from survey to survey are typical, with the likely source of the variation including underestimated photometric difference image uncertainties and excess scatter from bright host galaxy subtractions (as seen in R14 and Kessler et al 2015). Redshift evolution of the SN Ia population could also play a role.…”

“…Host galaxies. The observed flux scatter of SNe found in bright galaxies exceeds what is expected from Poisson noise alone (R14; Kessler et al 2015). To correct for this, SNANA adds host galaxy noise to SN flux uncertainties by placing each SN in a simulated host galaxy.…”

“…The distribution of PS1 host galaxies was determined from PS1 data; we measured the magnitudes and shape parameters of PS1 SN host galaxies using SExtractor, with zeropoints measured from the PS1 pipeline. We then use the noise model from Kessler et al (2015, their Equation (4)):…”