cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era
摘要
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 a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.
引用
@article{arxiv.2605.23839,
title = {cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era},
author = {Marco Bonici and Guadalupe Cañas-Herrera and Pedro Carrilho and Santiago Casas and Chiara Moretti and Andrea Pezzotta and Michel Aguena and Giovanni Aricó and Zahra Baghkhani and Matteo Baratto and Emilio Bellini and Jip de Buck and Klara Bertmann and Ben Bose and Jeger C. Broxterman and Pierre Burger and Carmelita Carbone and Chaitanya Chawak and Jose Coloma-Nadal and Martin Crocce and Stefano Davini and Christopher A. J. Duncan and Samuel Farrens and Lisa Goh and Nastassia Grimm and Alex Hall and Ryusei R. Kano and Martin Kärcher and Felicitas Keil and Raphaël Kou and Laila Linke and Arthur Loureiro and Katarina Markovic and David Navarro-Gironés and Filippo Oppizzi and Gabriele Parimbelli and Anna Porredon and Robert Reischke and Jaime Ruiz Zapatero and Iñigo Sáez-Casares and Ziad Sakr and Neel Shah and Davide Sciotti and Matthieu Schaller and Ivan Sladoljev and Arghavan Souki and Sankarshana Srinivasan and Konstantinos Tanidis and Peter L. Taylor and Nicolas Tessore and Linus Thummel and Maria Tsedrik and Isaac Tutusaus and Casper Vedder and Angus H. Wright and Miguel Zumalacárregui and Joe Zuntz},
journal= {arXiv preprint arXiv:2605.23839},
year = {2026}
}