24 citations · 33 across the 7 of their papers we have counts for
8 papers
ROBIN-PIP: Robust Bayesian Field-Level Inference with Physics-Informed Priors
Ludvig Doeser, Simon Ding, Guilhem Lavaux +1
Accurately extracting information from galaxy surveys, particularly at non-linear scales, requires increasingly complex and flexible forward models of the observed galaxy distribut…
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
Ludvig Doeser, Jens Jasche
Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data. While Markov chain Monte Carlo me…
The Manticore Project II: Bayesian digital twins of cosmic structure across the SDSS and BOSS volumes
Stuart McAlpine, Jens Jasche, Guilhem Lavaux +2
We present Manticore-Deep, a high-resolution Bayesian field-level reconstruction of cosmic large-scale structure over a comoving volume of to $z\approx…
Preparing for Rubin-LSST -- Detecting Brightest Cluster Galaxies with Machine Learning in the LSST DP0.2 simulation
Aline Chu, Ludvig Doeser, Simon Ding +1
The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of gal…
Learning the Universe: Tests of a Field Level -body Simulation Emulator
Matthew T. Scoggins, Matthew Ho, Francisco Villaescusa-Navarro +3
We apply and test a field-level emulator for non-linear cosmic structure formation in a volume matching next-generation surveys. Inferring the cosmological parameters and initial c…
Learning the Universe: Learning to Optimize Cosmic Initial Conditions with Non-Differentiable Structure Formation Models
Ludvig Doeser, Metin Ata, Jens Jasche
Making the most of next-generation galaxy clustering surveys requires overcoming challenges in complex, non-linear modelling to access the significant amount of information at smal…
COmoving Computer Acceleration (COCA): -body simulations in an emulated frame of reference
Deaglan J. Bartlett, Marco Chiarenza, Ludvig Doeser +1
-body simulations are computationally expensive, so machine-learning (ML)-based emulation techniques have emerged as a way to increase their speed. Although fast, surrogate mode…
Bayesian Inference of Initial Conditions from Non-Linear Cosmic Structures using Field-Level Emulators
Ludvig Doeser, Drew Jamieson, Stephen Stopyra +3
Analysing next-generation cosmological data requires balancing accurate modeling of non-linear gravitational structure formation and computational demands. We propose a solution by…