Optimal observational scheduling framework for binary and multiple stellar systems

TL;DR

This paper introduces a Bayesian, information-driven framework for scheduling optimal observations of binary and multiple stellar systems, enhancing efficiency in astronomical data collection.

Contribution

It presents a novel, computationally efficient Bayesian method based on maximum entropy sampling for optimal timing of observations in binary star systems.

Findings

Method is optimal in the Bayesian sense.

Effective in scheduling for visual and spectroscopic binaries.

Successfully applied to multiple stellar systems.

Abstract

The optimal instant of observation of astrophysical phenomena for objects that vary on human time-sales is an important problem, as it bears on the cost-effective use of usually scarce observational facilities. In this paper we address this problem for the case of tight visual binary systems through a Bayesian framework based on the maximum entropy sampling principle. Our proposed information-driven methodology exploits the periodic structure of binary systems to provide a computationally efficient estimation of the probability distribution of the optimal observation time. We show the optimality of the proposed sampling methodology in the Bayes sense and its effectiveness through direct numerical experiments. We successfully apply our scheme to the study of two visual-spectroscopic binaries, and one purely astrometric triple hierarchical system. We note that our methodology can be applied to any time-evolving phenomena, a particularly interesting application in the era of dedicated surveys, where a definition of the cadence of observations can have a crucial impact on achieving the science goals.