[Paper Review] Core Cosmology Library: Precision Cosmological Predictions for LSST

Nora Elisa Chisari, David Alonso+28 more|Edinburgh Research Explorer (University of Edinburgh)|Dec 14, 2018

Galaxies: Formation, Evolution, PhenomenaPhysics and Astronomy126 references52 citations

TL;DR

The Core Cosmology Library (CCL) provides high-accuracy cosmological predictions for LSST, including distances, power spectra, correlation functions, and halo statistics, with rigorous cross-validation against independent codes. It is open-source (C with Python interface) and validated for LSST-scale analyses.

ABSTRACT

The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of validation tests. Predictions are provided for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias and the halo mass function through state-of-the-art modeling prescriptions available in the literature. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. A rigorous validation procedure, based on comparisons between CCL and independent software packages, allows us to establish a well-defined numerical accuracy for each predicted quantity. As a result, predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is an open source software package written in C, with a python interface and publicly available at https://github.com/LSSTDESC/CCL.

Motivation & Objective

• Provide high-accuracy predictions of standard cosmological observables for LSST-scale analyses.
• Incorporate state-of-the-art modeling for distances, growth, power spectra, and two-point correlators across multiple cosmologies.
• Validate numerical accuracy of each observable via comparisons with independent software to establish reliable uncertainty budgets.
• Support fiducial LSST galaxy distributions and user-defined redshift distributions for photometric surveys.
• Enable interoperability with external tools (e.g., CLASS, Cosmic Emulator) and potential modified gravity extensions.

Proposed method

• Compute Hubble parameter H(a) and background quantities for flat and curved LCDM and wCDM cosmologies.
• Predict matter power spectrum P(k,z) using CLASS, Cosmic Emulator, or simple halo-model approaches with baryonic correction models.
• Calculate two-point angular power spectra and correlation functions across probes (number counts, galaxy shapes, CMB lensing) using transfer functions and line-of-sight integrals.
• Provide growth functions D(a) and f(a) by solving the growth differential equation with Runge-Kutta methods and interpolate with accelerated splines.
• Incorporate extensions such as non-zero curvature, massive neutrinos, evolving dark energy (CPL), and modified growth functions via Δf(a).
• Validate predictions by comparing to independent software and document numerical accuracy for each observable.

Experimental results

Research questions

• RQ1 What is the numerical accuracy of CCL predictions for distances, growth, and two-point statistics across standard and extended cosmologies?
• RQ2 How well do CCL outputs agree with independent packages (e.g., CLASS, Cosmic Emulator) for LSST-relevant observables?
• RQ3 Can CCL provide LSST-ready predictions for galaxy clustering, weak lensing, and their cross-correlations under realistic survey specifications?
• RQ4 How robust are CCL’s predictions to extensions such as massive neutrinos, curvature, and evolving dark energy when computing angular power spectra and correlation functions?
• RQ5 What are the practical guidance and limitations when using CCL in conjunction with external gravity codes or emulators?

Key findings

• CCL achieves high numerical accuracy by rigorous cross-validation against independent software for each observable.
• Predictions for galaxy clustering, galaxy–galaxy lensing, and cosmic shear are demonstrated to be within a fraction of the expected statistical uncertainty for LSST-scale analyses.
• CCL supports multiple cosmologies (flat/non-flat LCDM, CPL dark energy, massive neutrinos) and can link to external tools for extended models.
• Two-point correlators across probes include non-Limber computations and various contributions (density, RSD, magnification) for robust LSST modeling.
• Baryonic effects are incorporated via an effective BCM parametrization to account for AGN feedback and adiabatic cooling on the matter power spectrum.
• CCL is open-source (C with Python) and publicly available, enabling integration into LSST DESC pipelines and broader cosmological studies.