CITATION.cff

cff-version: 1.2.0
title: GWFAST
message: >-
  If you use this software, please cite it using the
  metadata from this file. Please also cite the
  provided references to the release papers.
type: software
authors:
  - given-names: Francesco
    family-names: Iacovelli
    email: francesco.iacovelli@unige.ch
    affiliation: University of Geneva
    orcid: https://orcid.org/0000-0002-4875-5862
  - given-names: Michele
    family-names: Mancarella
    email: michele.mancarella@unimib.it
    affiliation: University of Milan Bicocca
    orcid: https://orcid.org/0000-0002-0675-508X
repository-code: 'https://github.com/CosmoStatGW/gwfast'
url: 'https://github.com/CosmoStatGW/gwfast'
abstract: >-
  GWFAST is a Python code for forecasting the
  signal-to-noise ratios and parameter estimation
  capabilities of networks of gravitational-wave
  detectors, based on the Fisher information matrix
  approximation. It is designed for applications to
  third-generation gravitational-wave detectors. It
  is based on Automatic Differentiation, which makes
  use of the library JAX. This allows efficient
  parallelization and numerical accuracy. The code
  includes a module for parallel computation on
  clusters.
keywords:
  - python
  - automatic-differentiation
  - gravitational-waves
  - fisher-information
  - JAX
license: GPL-3.0
version: 1.0.2
date-released: 2022-09-08
doi: 10.5281/zenodo.7060235