most citedDistinguishing Coupled Dark Energy Models with Neural Networks

8 citations · 8 across the 4 of their papers we have counts for

collaborators

5 papers

astro-ph.CO2025

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 6: Impact of systematic uncertainties on the cosmological analysis

Euclid Collaboration, L. Blot, K. Tanidis +309

Extracting cosmological information from the Euclid galaxy survey will require modelling numerous systematic effects during the inference process. This implies varying a large numb…

astro-ph.CO2025

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 4: Validation and Performance

Euclid Collaboration, M. Martinelli, A. Pezzotta +334

The Euclid satellite will provide data on the clustering of galaxies and on the distortion of their measured shapes, which can be used to constrain and test the cosmological model.…

astro-ph.CO2025

Cosmology Likelihood for Observables in \Euclid (CLOE). 1. Theoretical recipe

Euclid Collaboration, V. F. Cardone, S. Joudaki +323

As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to…

astro-ph.CO2025

Phantom Crossing with Quintom Models

L. W. K. Goh, A. N. Taylor

We develop a two-scalar field quintom model, which utilises both a quintessence-like and a phantom-like scalar field, enabling a smooth and stable transition across the phan…

astro-ph.CO20248 cited

Distinguishing Coupled Dark Energy Models with Neural Networks

L. W. K. Goh, I. Ocampo, S. Nesseris +1

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a co…