English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Modern astronomical surveys have multiple competing scientific goals.Optimizing the observation schedule for these goals presents significantcomputational and theoretical challenges, and state-of-the-art methods rely onexpensive human inspection of simulated telescope schedules. Automated methods,such as reinforcement learning, have recently been explored to acceleratescheduling. However, there do not yet exist benchmark data sets oruser-friendly software frameworks for testing and comparing these methods. Wepresent DeepSurveySim -- a high-fidelity and flexible simulation tool for usein telescope scheduling. DeepSurveySim provides methods for tracking andapproximating sky conditions for a set of observations from a user-suppliedtelescope configuration. We envision this tool being used to produce benchmarkdata sets and for evaluating the efficacy of ground-based telescope schedulingalgorithms, particularly for machine learning algorithms that would suffer inefficacy if limited to real data for training.We introduce three example surveyconfigurations and related code implementations as benchmark problems that canbe simulated with DeepSurveySim.

DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling