3 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Knowledge Graph in Astronomical Research with Large Language Models: Quantifying Driving Forces in Interdisciplinary Scientific Discovery
Zechang Sun, Yuan-Sen Ting, Yaobo Liang +3
Identifying and predicting the factors that contribute to the success of interdisciplinary research is crucial for advancing scientific discovery. However, there is a lack of metho…
Zephyr : Stitching Heterogeneous Training Data with Normalizing Flows for Photometric Redshift Inference
Zechang Sun, Joshua S. Speagle, Song Huang +2
We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous…