33 citations · 65 across the 21 of their papers we have counts for
7 papers · 1 filter
Foundation Models for Astrophysics
Xiaosheng Zhao, Yuan-Sen Ting
Foundation models are high-capacity networks pretrained once on broad data and then reused across many tasks. This chapter introduces them through the idea of a transferable repres…
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…
Finetuning Stellar Spectra Foundation Models with LoRA
Xiaosheng Zhao, Yuan-Sen Ting, Alexander S. Szalay +1
Foundation models are beginning to impact stellar spectroscopy, where spectra encode rich physical information in a structured, language-like form. A key challenge is adapting thes…
FALCO: a Foundation model of Astronomical Light Curves for time dOmain astronomy
Xiaoxiong Zuo, Yihan Tao, Yang Huang +13
Time-domain surveys have advanced astronomical research by revealing diverse variable phenomena, from stellar flares to transient events. The scale and complexity of survey data, a…
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…
AstroMLab 1: Who Wins Astronomy Jeopardy!?
Yuan-Sen Ting, Tuan Dung Nguyen, Tirthankar Ghosal +8
We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 mu…
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…