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| Paper: |
Optimizing Observing Sequence Design for Periodic and Non-periodic Phenomena: A Bayesian Approach |
| Volume: |
347, Astronomical Data Analysis Software and Systems XIV |
| Page: |
529 |
| Authors: |
Johnston, M.D.; Knight, R. |
| Abstract: |
The problem of designing observing sequences to detect and characterize periodic phenomena occurs regularly in astronomical investigations. Examples of current interest include Cepheid variable searches in external galaxies (with Hubble Space Telescope), and future high accuracy astrometric observations of nearby stars with the Space Interferometry Mission (SIM) satellite to search for planetary companions. Various sampling strategies have been proposed to obtain good phase coverage over an interesting range of periods. Recently, Loredo and Chernoff have proposed the use of “Bayesian adaptive exploration”, a model-based Bayesian method that exploits observations made to date to determine the best future observation times according to a maximum information criterion. While this method makes the best possible use of any results already obtained, it does not address the “bootstrap” problem of scheduling in advance of any data collection. It also is highly compute-intensive, which is especially problematic when an integrated observing schedule for hundreds of targets is required, taking into account all of the various other constraints and preferences that come into play. In this paper we report on our progress on addressing these issues. We have developed an approximate expression for the uniformity of phase coverage that can be used when scheduling to assess candidate sample times. We describe the results obtained using this estimator, and compare them with detailed simulations. We describe our progress and plans for integrating optimizing criteria for both periodic and non-periodic observations into a single observation sequence. |
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