Ecosystem

DSF is developed within the LSST Dark Energy Science Collaboration (DESC) and is part of a broader ecosystem of scientific software for cosmological modelling, galaxy-galaxy lensing, and forecasting.

This page highlights selected packages that DSF depends on, as well as complementary tools for observational measurements, galaxy population modelling, covariance estimation, and cosmological analysis.

Dependencies

DSF builds on several scientific Python packages for cosmological modelling, survey tomography, and statistical inference.

The complete list of dependencies, including version requirements, is maintained in the pyproject.toml.

Cosmological modelling

Core Cosmology Library (CCL)

A cosmological modelling library developed within LSST DESC. CCL provides matter power spectra, correlation functions, halo-model calculations, and other cosmological quantities used in DSF predictions.

Survey tomography

Binny

A Python package for constructing and manipulating survey redshift distributions, tomographic bins, and bin combinations. DSF uses Binny for survey and tomography modelling.

Statistical inference

DerivKit

A Python package for numerical differentiation and derivative-based statistical inference, including Fisher information matrices and higher-order DALI approximations. DerivKit provides statistical forecasting capabilities used by DSF.

GetDist

A Python package for analysing and visualizing samples from probability distributions, particularly cosmological parameter posteriors. It provides tools for marginalized parameter constraints, credible regions, and posterior contour plots.

Related Software

The following packages address complementary aspects of cosmological modelling, observational measurements, galaxy populations, covariance estimation, and data analysis.

These packages are not required dependencies of DSF.

Galaxy-galaxy lensing measurements

dsigma

A Python package for measuring galaxy-galaxy lensing from observational catalogs. It estimates excess surface density around lens populations and supports jackknife covariance estimation and related summary statistics.

While DSF models and forecasts excess surface density, dsigma measures the observable from survey data.

Galaxy luminosity functions

LFKit

A Python toolkit for modelling galaxy luminosity functions, photometric corrections, magnitude conversions, and magnitude-limited catalog selection.

LFKit provides tools for computing galaxy number densities, completeness fractions, luminosity-function-weighted redshift trends, and observable or missing catalog populations.

Its applications include photometric redshift modelling, intrinsic alignments, galaxy cluster science, gravitational-wave cosmology, and survey forecasting.

Galaxy cluster modelling

CROW

The LSST-DESC Cluster Reconstruction of Observables Workbench is a Python library for predicting galaxy cluster observables. It provides complementary modelling capabilities for cluster-based cosmological analyses.

Covariance estimation

TJPCov

A Python package for calculating covariance matrices of cosmological two-point statistics. It provides complementary covariance modelling capabilities for large-scale structure and weak-lensing analyses.

Cosmological data formats

SACC

A Python library and standardized data format for storing cosmological summary statistics, covariance matrices, and associated metadata.

SACC supports the exchange of cosmological measurements and predictions between analysis tools.