1.5k citations · 4k across the 41 of their papers we have counts for
42 papers
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…
pop-cosmos: Star formation over 12 Gyr from generative modelling of a deep infrared-selected galaxy catalogue
Sinan Deger, Hiranya V. Peiris, Stephen Thorp +5
We study star formation over 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of 420,000 COSMOS2020 galaxies (IRAC Ch.1 ). The model learns distributi…
Impact of redshift distribution uncertainties on Lyman-break galaxy cosmological parameter inference
Francesco Petri, Boris Leistedt, Daniel J. Mortlock +5
A significant number of Lyman-break galaxies (LBGs) with redshifts 3 < z < 5 are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (…
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,…
pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
Stephen Thorp, Justin Alsing, Hiranya V. Peiris +5
We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pre-trained population model (pop-cosmos) that was calibrated…
Scaling-laws for Large Time-series Models
Thomas D. P. Edwards, James Alvey, Justin Alsing +2
Scaling laws for large language models (LLMs) have provided useful guidance in training ever larger models for predictable performance gains. Time series forecasting shares a simil…