3 papers
astro-ph.GA2026
Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
Chenrui Ma, Zechang Sun, Tao Jing +4
Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models…
astro-ph.IM2025
Mephisto: Self-Improving Large Language Model-Based Agents for Automated Interpretation of Multi-band Galaxy Observations
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Astronomical research has long relied on human expertise to interpret complex data and formulate scientific hypotheses. In this study, we introduce Mephisto -- a multi-agent collab…
astro-ph.IM2025
Interpreting Multi-band Galaxy Observations with Large Language Model-Based Agents
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Astronomical research traditionally relies on extensive domain knowledge to interpret observations and narrow down hypotheses. We demonstrate that this process can be emulated usin…