pith. sign in

arxiv: 1801.03177 · v1 · pith:G4X6II2Qnew · submitted 2018-01-09 · 🌌 astro-ph.IM

The Dark Energy Survey Image Processing Pipeline

classification 🌌 astro-ph.IM
keywords pipelinesurveyimageimagingopticalbandsdarkdata
0
0 comments X p. Extension
pith:G4X6II2Q Add to your LaTeX paper What is a Pith Number?
\usepackage{pith}
\pithnumber{G4X6II2Q}

Prints a linked pith:G4X6II2Q badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

read the original abstract

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 square degree 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 square degrees. 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies

    astro-ph.GA 2026-05 unverdicted novelty 6.0

    Domain adaptation with an ensemble of CNN and transformer models trained on DES detects 20,180 LSBGs and 434 UDGs in KiDS DR5, with structural parameters and environmental trends consistent with known samples.