7 papers
Growth, geometry, and early-universe split of the matter density parameter
Felicitas Keil, Isaac Tutusaus, Alain Blanchard
While the cold dark matter (CDM) model can successfully reproduce the measurements of many cosmological probes, some discrepancies have recently emerged. It is therefore nec…
Cosmology beyond standard sirens: cross-correlation of gravitational waves and neutral hydrogen intensity mapping
Matteo Schulz, Ulyana Dupletsa, Andrea Cozzumbo +8
We explore the potential of cross-correlation between gravitational wave (GW) events and neutral hydrogen (HI) intensity mapping surveys to serve as an independent cosmological pro…
cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era
Marco Bonici, Guadalupe Cañas-Herrera, Pedro Carrilho +16
cloelike is a Python package providing modular, composable Gaussian likelihood classes for the main cosmological large-scale structure observables targeted by the ESA Euclid space…
cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era
Marco Bonici, Guadalupe Cañas-Herrera, Pedro Carrilho +54
cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters…
Euclid preparation. XCV. Cosmology Likelihood for Observables in Euclid (CLOE). 2. Code implementation
Euclid Collaboration, S. Joudaki, V. Pettorino +345
We provide a description of the code implementation and structure of Cosmology Likelihood for Observables in Euclid (CLOE), developed by members of the Euclid Consortium. CLOE is a…
Probing the Distance Duality Relation with Machine Learning and Recent Data
Felicitas Keil, Savvas Nesseris, Isaac Tutusaus +1
The distance duality relation (DDR) relates two independent ways of measuring cosmological distances, namely the angular diameter distance and the luminosity distance. These can be…
Classification of Fermi-LAT blazars with Bayesian neural networks
Anja Butter, Thorben Finke, Felicitas Keil +2
The use of Bayesian neural networks is a novel approach for the classification of gamma-ray sources. We focus on the classification of Fermi-LAT blazar candidates, which can be div…