The Liverpool Telescope (LT) provides robotic, autonomous observations for the time-domain community with rapidly reduced data available to users minutes after observations are taken. An intelligent dispatch scheduler, developed in the early 2000s, ranks observations according to various factors such as observational constraints, position on the sky, weather and scientific priority. However, there is a requirement to exploit advancements in Artificial Intelligence to optimise robotic follow-up strategy and meet the demands of our ever-growing survey telescope domain with an intelligent scheduling solution.

The New Robotic Telescope (NRT), a 4-metre facility designed for rapid classification of transients, combines mechanical, Artificial Intelligence and hardware advances to create an extremely rapid response facility, slewing across the sky and acquiring data within 30 seconds of trigger receipt. The scheduler is crucial in streamlining the follow-up process. We can exploit the vast LT database of observations to develop optimal scheduling algorithms before the NRT’s first light in 2026.

Each LT observation carries many associated data in FITS headers and ancillary data, such as weather reports and duty officer logs. These are often stored in a distributed manner where relationships within the data are time-consuming to extract. In this project, we have used Microsoft Power BI to create relational links between these data: over 594 unique FITS headers from approximately 4.1 million observations. We can then query the relational tables in near real-time to generate subsets of these data where valuable insights into the operation of the LT can be gained. These subsets of data can be used to train several different algorithms to uncover hidden trends and facilitate real-time intelligent scheduling.

In this talk, I will present the methodology and early findings from this project and discuss how these insights will help to inform future work in building optimal scheduling algorithms for the New Robotic Telescope.