ASTER -- Agentic Science Toolkit for Exoplanet Research
Emilie Panek, Alexander Roman, Gaurav Shukla, Leonardo Pagliaro, Katia Matcheva, Konstantin Matchev

TL;DR
ASTER is an AI-powered framework that streamlines exoplanet atmospheric analysis by integrating tools, managing workflows, and assisting users with complex data interpretation tasks.
Contribution
It introduces ASTER, a novel orchestration framework that enables LLM-driven interaction with domain-specific tools for exoplanet research workflows.
Findings
Successfully demonstrated on WASP-39b data
Automates retrievals and spectral modeling tasks
Enhances accessibility for exoplanet atmospheric analysis
Abstract
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…
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