8 papers
Large language models are not the problem
Hiranya V. Peiris
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes…
pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations
Boris Leistedt, Hiranya V. Peiris, Anik Halder +13
The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this chal…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis
Boris Leistedt, Matthew R. Becker, Humna Awan +14
The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade, supplying unprec…
Galaxies as stochastic systems: why the next breakthrough in galaxy evolution requires one hundred million spectra
Sandro Tacchella, Vasily Belokurov, Harry T. J. Bevins +4
Each galaxy is observed only once along its life, making galaxy evolution fundamentally an inverse statistical problem: time-dependent physics must be inferred from ensembles of si…
pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
Stephen Thorp, Hiranya V. Peiris, Gurjeet Jagwani +6
We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift . The model is trained on distributions of observed colors and magnitudes,…