From the 1 of 6 linked papers with an AI index.
1 citations · 1 across the 1 of their papers we have counts for
6 papers
DESI constraints on two-field quintessence with exponential potentials
George Alestas, Marienza Caldarola, Indira Ocampo +2
The paper examines a dark‑energy model with two scalar fields having double‑exponential potentials that together behave like a single shallow potential, and tests its viability usi…
Explaining Neural Networks on the Sky: Machine Learning Interpretability for Cosmic Microwave Background Maps
Indira Ocampo, Guadalupe Cañas-Herrera, Guadalupe Cañas-Herrera
We present a framework for cosmological model selection using Neural Networks (NNs) trained directly on simulated Cosmic Microwave Background (CMB) temperature and polarisation map…
Forecast constraints on null tests of the CDM model with SPHEREx
Alejandro Mata Román, Indira Ocampo, Savvas Nesseris
In this work we quantify the ability of the upcoming SPHEREx survey to constrain cosmological observables and test the internal consistency of the cosmological constant and cold da…
Euclid: Forecasts on CDM consistency tests with growth rate data
I. Ocampo, D. Sapone, S. Nesseris +156
The large-scale structure (LSS) of the Universe is an important probe for deviations from the canonical cosmological constant and cold dark matter (CDM) model. A statistic…
Neural Networks for cosmological model selection and feature importance using Cosmic Microwave Background data
I. Ocampo, G. Cañas-Herrera, S. Nesseris
The measurements of the temperature and polarisation anisotropies of the Cosmic Microwave Background (CMB) by the ESA Planck mission have strongly supported the current concordance…
Enhancing Cosmological Model Selection with Interpretable Machine Learning
Indira Ocampo, George Alestas, Savvas Nesseris +1
We propose a novel approach using neural networks (NNs) to differentiate between cosmological models, and implemented LIME as an interpretability approach to identify the key featu…