Jun 26 – 27, 2023
America/Chicago timezone

Telescopes Drive Themselves: Optimizing Cosmic Survey Scheduling with Reinforcement Learning

Jun 27, 2023, 9:45 AM
One West (Fermilab)

One West



Franco Terranova Margaret Voetberg (Fermi National Accelerator Laboratory)


The planning and execution of observational cosmology campaigns have undergone a substantial increase in complexity, particularly for advanced telescopes like the Rubin Observatory’s LSST, JWST, and the Nancy Grace Roman telescope.
Traditionally, astronomical observatories have relied on manual planning to scan a predefined list of astronomical objects, which usually results in suboptimal observations.
We are developing a framework for statistical learning-based optimization of telescope pointings to gather data that is most useful for a pre-defined scientific reward.
We frame the observational campaign as a Markovian Decision Process, which captures the nature of sequential decision-making.
We implement this through reinforcement learning (RL), which has emerged in the field of artificial intelligence as a powerful approach to training autonomous systems.
In this study, we focus on the application of RL algorithms on an offline dataset containing simulated observations with a discrete set of sky locations the telescope is allowed to visit, referred to as the “action space.”
Two key aspects are investigated: 1) the preprocessing of the dataset using normalization techniques and potential observation space reduction, and 2) the application of value-based networks for decision-making.
Considering the range of well-known RL algorithms, this study has mainly targeted value-based networks, and in particular Deep Q-Networks (DQNs), since they outperform policy-based networks on the offline dataset.
Our experimental results demonstrate that the combination of preprocessing techniques, along with value-based networks, yields high performances and capabilities to generalize on unseen data for our task.
Furthermore, the analysis highlighted how varying certain hyperparameters led to a significant impact on the obtained results.
Our results contribute to the advancement of autonomous systems, specifically in the context of process scheduling.

Primary author


Margaret Voetberg (Fermi National Accelerator Laboratory) Brian Nord (Fermilab) Eric Neilsen (Fermilab)

Presentation materials