From the 1 of 5 linked papers with an AI index.
5 papers
Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks
Ashutosh Kumar Mishra, Emma Tolley, Nicolas Cerardi
The paper introduces a physics‑informed generative U‑Net that can evolve fuzzy dark matter fields and perform super‑resolution of simulations while enforcing the Schrödinger‑Poisso…
Forecasting the occupancy of satellite megaconstellations in SKA observations
Nicolas Cerardi, Emma Tolley, Federico di Vruno
The Square Kilometre Array (SKA) is expected to start science operations in 2030 and by that time there could be up to 10 artificial satellites in Earth's orbit, comprising an…
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…