Accurate waveform models for gravitational-wave astrophysics: synergetic approaches from analytical relativity

TL;DR

This paper discusses the development of highly accurate waveform models for gravitational-wave astrophysics, crucial for extracting detailed astrophysical and cosmological information from current and future GW detectors.

Contribution

It introduces synergetic approaches combining analytical relativity methods to improve waveform accuracy for gravitational-wave data analysis.

Findings

Enhanced waveform models improve detection sensitivity.

Better parameter estimation accuracy.

Facilitates testing of general relativity in strong fields.

Abstract

Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, several compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron stars are formed, what neutron stars are composed of, how the Universe expands, and allow testing general relativity in the highly-dynamical strong-field regime. It is the goal of GW astrophysics to extract such information as accurately as possible. Yet, this is only possible if the tools and technology used to detect and analyze GWs are advanced enough. A key aspect of GW searches are waveform models, which encapsulate our best predictions for the gravitational radiation under a certain set of parameters, and that need to be cross-correlated with data to extract GW signals. Waveforms must be very accurate to avoid missing important physics in the data, which might be the key to answer the fundamental questions of GW astrophysics. The continuous improvements of the current LIGO-Virgo detectors, the development of next-generation ground-based detectors such as the Einstein Telescope or the Cosmic Explorer, as well as the development of the Laser Interferometer Space Antenna (LISA), demand accurate waveform models. [Abridged.]