Science-Driven Optimization of the LSST Observing Strategy
LSST Science Collaborations: Phil Marshall, Timo Anguita, Federica B., Bianco, Eric C. Bellm, Niel Brandt, Will Clarkson, Andy Connolly, Eric, Gawiser, Zeljko Ivezic, Lynne Jones, Michelle Lochner, Michael B. Lund,, Ashish Mahabal, David Nidever, Knut Olsen, Stephen Ridgway

TL;DR
This paper discusses optimizing the LSST observing strategy using simulations and metrics to enhance scientific outcomes across various astronomy projects, emphasizing the potential benefits of a non-uniform, rolling cadence approach.
Contribution
It introduces a framework for evaluating LSST observing strategies through quantitative metrics, enabling systematic, science-driven optimization of the survey schedule.
Findings
Rolling cadence may benefit time domain and moving object science.
Quantitative metrics facilitate comparison of different observing strategies.
Exploring non-uniform sky tiling could improve scientific returns.
Abstract
The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Adaptive optics and wavefront sensing · Gamma-ray bursts and supernovae
