8 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…
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: 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: 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…