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CITATION.cff
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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