Speaker
Description
Astrophysics is entering a data-rich era driven by multi-wavelength observatories and multi-messenger experiments. These facilities produce vast, heterogeneous datasets that challenge traditional analysis pipelines. General-purpose AI systems, while powerful, often lack the contextual reasoning and scientific rigor required for astrophysical interpretation. AstroGenesis is an AI-powered, domain-aware multi-agent research assistant designed to revolutionize how astrophysicists access, analyze, and interpret astronomical data. Built upon a Retrieval-Augmented Generation (RAG) framework and a modular multi-agent architecture, AstroGenesis integrates literature retrieval, data access, theoretical modeling, and hypothesis generation into a unified ecosystem. Each specialized agent autonomously handles dedicated tasks-such as spectral fitting, time-series analysis, or model inference-under the coordination of a central supervisory agent that ensures transparency and reproducibility. Key innovations include a domain-aware RAG system that grounds responses in peer-reviewed literature; seamless integration with multi-wavelength and multi-messenger archives for both raw and processed data; and neural-network-based modeling agents trained on large-scale radiative simulations for real-time, physics-consistent inference. It is possible to inspect the machinery with a human-on-the-loop for validation, and feedback to further enhance reliability. Demonstrated through a prototype for blazar research, AstroGenesis can be generalized to diverse astrophysical phenomena. By unifying reasoning, data retrieval, and theoretical modeling within a scalable framework, it lowers the barrier to advanced analysis and fosters transparent, reproducible, cross-disciplinary discovery in modern astrophysics.
