Science-Driven Optimization of the LSST Observing Strategy
Document Type
Article
Publication Date
8-1-2017
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 to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible.
Publication Source (Journal or Book title)
ArXiv e-prints
Recommended Citation
Collaboration, L. S., Marshall, P., Anguita, T., Bianco, F. B., Bellm, E. C., Brandt, N., Clarkson, W., Connolly, A., Gawiser, E., Ivezic, Z., Jones, L., Lochner, M., Lund, M. B., Mahabal, A., Nidever, D., Olsen, K., Ridgway, S., Rhodes, J., Shemmer, O., Trilling, D., Vivas, K., Walkowicz, L., Willman, B., Yoachim, P., Anderson, S., Antilogus, P., Angus, R., Arcavi, I., Awan, H., Biswas, R., Bell, K. J., Bennett, D., & Britt, C. (2017). Science-Driven Optimization of the LSST Observing Strategy. ArXiv e-prints Retrieved from https://repository.lsu.edu/physics_astronomy_pubs/6383