Why do we do astrophysics?

TL;DR

This white paper examines how the rapid rise of large language models (LLMs) intersects with astrophysics, arguing that the field should be guided by clear principles—novelty, people-centricity, trust, rigorous methods, and recognition of non-clinical value. It frames astrophysics as literature-driven with a strong emphasis on provenance, responsibility, and ethical considerations, while acknowledging the increasing data-science orientation of the discipline. The work surveys a broad constellation of benefits—from public engagement and workforce development to open science and global collaboration—alongside two extreme policy options (let-them-cook and ban-and-punish) and a potential middle ground centered on transparency and best practices. Ultimately, the paper contends that the question is not only how we use AI in astrophysics, but why we do astrophysics at all, urging thoughtful, human-centered policy and practice to sustain the field’s intellectual and educational value.

Abstract

At time of writing, large language models (LLMs) are beginning to obtain the ability to design, execute, write up, and referee scientific projects on the data-science side of astrophysics. What implications does this have for our profession? In this white paper, I list - and argue for - a set of facts or "points of agreement" about what astrophysics is, or should be; these include considerations of novelty, people-centrism, trust, and (the lack of) clinical value. I then list and discuss every possible benefit that astrophysics can be seen as bringing to us, and to science, and to universities, and to the world; these include considerations of love, weaponry, and personal (and personnel) development. I conclude with a discussion of two possible (extreme and bad) policy recommendations related to the use of LLMs in astrophysics, dubbed "let-them-cook" and "ban-and-punish." I argue strongly against both of these; it is not going to be easy to develop or adopt good moderate policies.