7 papers · 1 filter
pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches
Benedict Van den Bussche, Sinan Deger, Hiranya V. Peiris +6
The physical processes that shape a galaxy's spectrum are strongly degenerate in observations, obscuring which processes act independently. Leveraging the pop-cosmos generative gal…
pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web
Madalina N. Tudorache, Hiranya V. Peiris, Stephen Thorp +7
Galaxy sizes are correlated with stellar mass and redshift, as characterised by size scaling relations. The inferred forms of these scaling relations are sensitive to how galaxies…
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…
pop-cosmos: Redshifts and physical properties of KiDS-1000 galaxies
Anik Halder, Hiranya V. Peiris, Stephen Thorp +8
Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying th…
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: A comprehensive picture of the galaxy population from COSMOS data
Justin Alsing, Stephen Thorp, Sinan Deger +4
We present pop-cosmos: a comprehensive model characterizing the galaxy population, calibrated to ( selected) galaxies from the Cosmic Evolution Survey (COSMOS) with…