5 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks
Ashutosh Kumar Mishra, Emma Tolley, Nicolas Cerardi
Generative machine learning models have recently emerged as powerful tools for producing cosmological simulations. However, many existing emulators do not explicitly enforce the un…
Simulation-Based Cosmological Mass Calibration of XXL Galaxy Clusters using HSC Weak Lensing
Sut-Ieng Tam, Keiichi Umetsu, Adam Amara +13
We present a cosmological analysis of the X-ray-selected galaxy cluster sample from the XXL survey, employing a simulation-based inference (SBI) framework to jointly constrain cosm…
Solving the Cosmological Vlasov-Poisson Equations with Physics-Informed Kolmogorov-Arnold Networks
Nicolas Cerardi, Emma Tolley, Ashutosh Mishra
Cold dark matter (CDM) evolves as a collisionless fluid under the Vlasov-Poisson equations, but N-body simulations approximate this evolution by discretising the distribution funct…
The Cosmological analysis of X-ray cluster surveys VII. Bypassing scaling relations with Lagrangian Deep Learning and Simulation-based inference
Nicolas Cerardi, Marguerite Pierre, François Lanusse +1
Galaxy clusters, the pinnacle of structure formation in our universe, are a powerful cosmological probe. Several approaches have been proposed to express cluster number counts, but…
The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams
M. Kosiba, N. Cerardi, M. Pierre +4
The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe. Cosmological analyses with galaxy clusters traditionally emplo…