7 papers
Your Outie Is a Wonderful Astronomer: Macrodata Refinement of the Astro-ph ArXiv Feed at Phermon Industries
Yuan-Sen Ting
We present the Severed Floor, a framework for Macrodata Refinement of the daily astro-ph arXiv feed, deployed at Phermon Industries (formerly McPherson Laboratory, The Ohio State U…
What Understanding Means in AI-Laden Astronomy
Yuan-Sen Ting, André Curtis-Trudel, Siyu Yao
Artificial intelligence is rapidly transforming astronomical research, yet the scientific community has largely treated this transformation as an engineering challenge rather than…
Why Machine Learning Models Systematically Underestimate Extreme Values II: How to Fix It with LatentNN
Yuan-Sen Ting
Attenuation bias -- the systematic underestimation of regression coefficients due to measurement errors in input variables -- affects astronomical data-driven models. For linear re…
Statistical Machine Learning for Astronomy -- A Textbook
Yuan-Sen Ting
This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that…
Teaching Astronomy with Large Language Models
Yuan-Sen Ting, Teaghan O'Briain
We present a study of LLM integration in final-year undergraduate astronomy education, examining how students develop AI literacy through structured guidance and documentation requ…
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
Franck Cappello, Sandeep Madireddy, Robert Underwood +23
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that req…