We report the discovery of eight new Milky Way companions in~1800 deg 2 of optical imaging data collected during the first year of the Dark Energy Survey (DES). Each system is identified as a statistically significant overdensity of individual stars consistent with the expected isochrone and luminosity function of an old and metal-poor stellar population. The objects span a wide range of absolute magnitudes (M V from -2.2 to -7.4 mag), physical

We describe the implementation and optimization of the ESSENCE supernova survey, which we have undertaken to measure the equation of state parameter of the dark energy. We present a method for optimizing the survey exposure times and cadence to maximize our sensitivity to the dark energy equation of state parameter w = P/ρc 2 for a given fixed amount of telescope time. For our survey on the CTIO 4m telescope, measuring the luminosity distances and redshifts for -3supernovae at modest redshifts (z ∼ 0.5 ± 0.2) is optimal for determining w. We describe the data analysis pipeline based on using reliable and robust image subtraction to find supernovae automatically and in near real-time. Since making cosmological inferences with supernovae relies crucially on accurate measurement of their brightnesses, we describe our efforts to establish a thorough calibration of the CTIO 4m natural photometric system.In its first four years, ESSENCE has discovered and spectroscopically confirmed 102 type Ia SNe, at redshifts from 0.10 to 0.78, identified through an impartial, effective methodology for spectroscopic classification and redshift determination. We present the resulting light curves for the all type Ia supernovae found by ESSENCE and used in our measurement of w, presented in Wood-Vasey et al. (2007).

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ∼ 5000 deg 2 survey of the southern sky in five optical bands (g, r, i, z,Y ) to a depth of ∼24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over ∼ 27 deg 2 . DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.

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