Why do we do astrophysics?
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. This doc
Why do we do astrophysics? by David W. Hogg 1 1 1 The author is at the Center for Cosmology and Particle Physics, Department of Physics, New York University; and at the Center for Computational Astronomy, Flatiron Institute; and at the Max-Planck-Institut für Astronomie, Heidelberg. I am thankful to Roger Blandford (Stanford), Mike Blanton (Carnegie), Josh Bloom (Berkeley), Gaby Contardo (Nova Gorica), Matt Daunt (NYU), Leslie Greengard (Flatiron), Juna Kollmeier (Carnegie), Jim Peebles (Princeton), Adrian Price-Whelan (Flatiron), Hans-Walter Rix (MPIA), and Kate Storey-Fisher (Stanford) for d
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