21 citations · 42 across the 13 of their papers we have counts for
13 papers
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…
Rapid and robust simulation-based inference for kilonovae
Stephanie M. Brown, Mattia Bulla, Hiranya V. Peiris +6
With the next generation of both electromagnetic and gravitational wave observatories beginning to come online, rapid analysis methods for kilonova data are becoming increasingly i…
Thermostats, Not Engines: A New Picture of Halo Gas Regulation
Hiranya V. Peiris, Andrew Pontzen, Madalina N. Tudorache +5
We propose that black hole feedback regulates gas in massive halos by establishing an entropy ceiling; the resulting buoyant gas migrates to the virial radius with no additional en…
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
Gurjeet Jagwani, Stephen Thorp, Sinan Deger +1
Diffusion and flow-based models are ubiquitously used for generative modelling and density estimation. They admit a deterministic probability flow ordinary differential equation (P…
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: 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: 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…
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…
Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles
Stephen Thorp, Hiranya V. Peiris, Daniel J. Mortlock +3
We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare th…