ASTER -- Agentic Science Toolkit for Exoplanet Research
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing atmospheric composition of transiting exoplanets. The analyses of these data require the combination of archival queries, literature search, the use of radiative transfer models, and Bayesian retrieval frameworks, each demanding specialized expertise. Modern large language models enable the coordinated execution of complex, multi-step tasks by AI agents with tool integration, structured prompts, and iterative reasoning. In this study we present ASTER, an Agentic Science Toolkit for Exoplanet Research. ASTER is an orchestration framework that brings LLM capability to the exoplanetary community by enabling LLM-driven interaction with integrated domain-specific tools, workflow planning and management, and support for common data analysis tasks. Currently ASTER incorporates tools for downloading planetary parameters and observational datasets from the NASA Exoplanet Archive, as well as the generation of transit spectra from the TauREx radiative transfer model, and the completion of Bayesian retrieval of planetary parameters with TauREx. Beyond tool integration, the agent assists users by proposing alternative modeling approaches, reporting potential issues and suggesting solutions, and interpretations. We demonstrate ASTER's workflow through a complete case study of WASP-39b, performing multiple retrievals using observational data available on the archive. The agent efficiently transitions between datasets, generates appropriate forward model spectra and performs retrievals. ASTER provides a unified platform for the characterization of exoplanet atmospheres. Ongoing development and community contributions will continue expanding ASTER's capabilities toward broader applications in exoplanet research.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 61 references that are in the map. First posted on arXiv 27 Mar 2026.
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References · 8
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 478
- 2017Collaborative Nested Sampling: Big Data vs. complex physical modelscited by 4
- 2023Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detectioncited by 3
- 2023Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Modelcited by 3
- 2026Orchestral AI: A Framework for Agent Orchestrationcited by 2
- 2025Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopycited by 1
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