$\texttt{GWFAST}$: a Fisher information matrix Python code for third-generation gravitational-wave detectors

TL;DR

GWFAST is a Python tool that efficiently estimates gravitational-wave source parameters using Fisher information matrices, incorporating Earth's motion effects and automatic differentiation for high accuracy, aiding third-generation detector forecasts.

Contribution

The paper introduces GWFAST, a novel Python code that improves parameter estimation for gravitational waves by including Earth's motion effects and using automatic differentiation.

Findings

Efficient parameter estimation for large gravitational-wave catalogs.

Inclusion of Earth's motion effects in Fisher matrix calculations.

High-accuracy derivatives via automatic differentiation.

Abstract

We introduce $\texttt{GWFAST}$, a Fisher information matrix $\texttt{Python}$ code that allows easy and efficient estimation of signal-to-noise ratios and parameter measurement errors for large catalogs of resolved sources observed by networks of gravitational-wave detectors. In particular, $\texttt{GWFAST}$ includes the effects of the Earth's motion during the evolution of the signal, supports parallel computation, and relies on automatic differentiation rather than on finite differences techniques, which allows the computation of derivatives with accuracy close to machine precision. We also release the library $\texttt{WF4Py}$ implementing state-of-the-art gravitational-wave waveforms in $\texttt{Python}$. In this paper we provide a documentation of $\texttt{GWFAST}$ and $\texttt{WF4Py}$ with practical examples and tests of performance and reliability. In a companion paper we present forecasts for the detection capabilities of the second and third generation of ground-based gravitational-wave detectors, obtained with $\texttt{GWFAST}$.