Optimal real-time scheduling for observations of transients across global telescope networks

全球望远镜网络瞬变观测的最佳实时调度

• 批准号: 2095422
• 金额: --
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2095422
• 关键词: Optimal; real; time; scheduling; observations

World-spanning networks of robotic telescopes have opened up new opportunities for the regime of time-domain astronomy. The deployment of such networks comes along with the roll-out of a new generation of powerful surveys that provide "transient factories", calling for prompt and extensive follow-up monitoring. How can such resources be used efficiently and how can observations be coordinated? While the scheduling of telescopes so far has mostly been discussed from the perspective of service providers, we need to look at this from the perspective of the community of users who want to achieve a scientific objective using a diversity of resources coming from different providers. The selection of a specific target to be observed at a specific time by a specific instrument needs to take into account: 1) the science goals, 2) the acquired data on all potentially relevant targets, 3) the technical specifications of the instrument, 4) the observability of the targets. In particular, target priorities will change as fast as the targets vary themselves and data therefore need to be analysed in quasi-real time in order not to miss critical features. Consequently, a large amount of data need to be pulled together and processed immediately, while continuous monitoring requires uninterrupted operability. Within larger user communities, objective functions and resulting monitoring strategies also need to consider the ownership of data and both individual and collective benefits. The University of St Andrews has pioneered the implementation of automated target selection strategies for the follow-up of ongoing gravitational microlensing events with the LCOGT/SUPAscope and MiNDSTEp networks. Students will face a technology challenge on data processing, modelling, and management at the intersection of astronomy and computer science, matching the requirement to achieve a fast throughput. Solutions to this sort of problem will likely to be transferable to serve applications in other areas.

遍布世界的机器人望远镜网络为时域天文学制度开辟了新的机会。在部署这种网络的同时，沿着推出了新一代强有力的调查，提供“临时工厂”，要求进行迅速和广泛的后续监测。如何有效利用这些资源，如何协调观测？虽然到目前为止，望远镜的时间安排主要是从服务提供者的角度讨论的，但我们需要从希望利用来自不同提供者的各种资源实现科学目标的用户群体的角度来看待这个问题。选择在特定时间用特定仪器观测的特定目标需要考虑到：1）科学目标，2）所获得的关于所有潜在相关目标的数据，3）仪器的技术规格，4）目标的可观测性。具体而言，目标优先次序的变化与目标本身的变化一样快，因此需要准实时地分析数据，以免遗漏关键特征。因此，需要将大量数据汇集在一起并立即处理，而连续监控需要不间断的可操作性。在较大的用户群体中，目标函数和由此产生的监测战略还需要考虑数据的所有权以及个人和集体的利益。圣安德鲁斯大学率先实施了自动目标选择策略，用于使用LCOGT/SUPAscope和MiNDSTEp网络跟踪正在进行的引力微透镜事件。学生将面临天文学和计算机科学交叉点的数据处理，建模和管理方面的技术挑战，以满足实现快速吞吐量的要求。这类问题的解决方案很可能可以转移到其他领域的应用中。