6 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
Explaining Neural Networks on the Sky: Machine Learning Interpretability for Cosmic Microwave Background Maps
Indira Ocampo, 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…
DESI constraints on two-field quintessence with exponential potentials
George Alestas, Marienza Caldarola, Indira Ocampo +2
We investigate a quintessence model involving two scalar fields with double-exponential potentials. This configuration allows the system as a whole to emulate the dynamics of a sin…
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 statistical…
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…
Non-Linearity-Free prediction of the growth-rate using Convolutional Neural Networks
Koya Murakami, Indira Ocampo, Savvas Nesseris +2
The growth-rate of the large-scale structure of the Universe is an important dynamic probe of gravity that can be used to test for deviations from General Relativity. How…