Improving the LSST dithering pattern and cadence for dark energy studies

@inproceedings{Carroll2014ImprovingTL,
  title={Improving the LSST dithering pattern and cadence for dark energy studies},
  author={Christopher M. Carroll and Eric Gawiser and Peter Kurczynski and Rachel A. Bailey and Rahul Biswas and David Anthony Cinabro and Saurabh W. Jha and R. Lynne Jones and K. Simon Krughoff and Aneesa Sonawalla and W. Michael Wood-Vasey},
  booktitle={Astronomical Telescopes and Instrumentation},
  year={2014}
}
The Large Synoptic Survey Telescope (LSST) will explore the entire southern sky over 10 years starting in 2022 with unprecedented depth and time sampling in six filters, ugrizy. Artificial power on the scale of the 3.5 deg LSST field-of-view will contaminate measurements of baryonic acoustic oscillations (BAO), which fall at the same angular scale at redshift z ~ 1. Using the HEALPix framework, we demonstrate the impact of an “un- dithered” survey, in which 17% of each LSST field-of-view is… 
Testing LSST Dither Strategies for Survey Uniformity and Large-Scale Structure Systematics
The Large Synoptic Survey Telescope (LSST) will survey the southern sky from 2022--2032 with unprecedented detail. Since the observing strategy can lead to artifacts in the data, we investigate the
Optimizing LSST observing strategy for weak lensing systematics
The LSST survey will provide unprecedented statistical power for measurements of dark energy. Consequently, controlling systematic uncertainties is becoming more important than ever. The LSST
Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a
Mapping and simulating systematics due to spatially-varying observing conditions in DES Science Verification data
Spatially varying depth and the characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses,
SNIa detection in the SNLS photometric analysis using Morphological Component Analysis
TLDR
A subtracted image stack treatment is developed to reduce the number of non SN-like events using morphological component analysis and set up a new detection strategy to obtain coordinates with good resolution for the extracted signal.
Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
TLDR
Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy.
Reengineering observatory operations for the time domain
TLDR
The astronomical community is entering an era of rapid change increasingly characterized by large time domain surveys, robotic telescopes and automated infrastructures, and – most significantly – of operating modes and scientific consortia that span the authors' individual facilities, joining them into complex network entities.
Optimizing the LSST Observing Strategy for Dark Energy Science : DESC Recommendations for the Wide-Fast-Deep Survey
Author(s): Lochner, Michelle; Scolnic, Daniel M; Awan, Humna; Regnault, Nicolas; Gris, Philippe; Mandelbaum, Rachel; Gawiser, Eric; Almoubayyed, Husni; Setzer, Christian N; Huber, Simon; Graham,
The LSST metrics analysis framework (MAF)
TLDR
The Metrics Analysis Framework is described, an open-source python framework developed to provide a user-friendly, customizable, easily-extensible set of tools for analyzing data sets, and an example of using MAF to evaluate different dithering strategies.
EXAMPLES OF COMMUNITY SYSTEM ISSUES FROM THIS CONFERENCE
  • 2014

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