PRINCESS: Prediction of compact binaries observations with gravitational waves

TL;DR

PRINCESS is a computational tool that predicts gravitational wave signals from compact binary coalescences, including individual events and the astrophysical background, for current and future detector networks, aiding in astrophysical model constraints.

Contribution

It introduces a novel, comprehensive prediction framework combining event and background forecasts using user-provided catalogs for current and future gravitational wave detectors.

Findings

Forecasts of binary black hole detections for future observatories

Predicted stochastic background spectra from unresolved sources

Potential to resolve nearly all CBC events with advanced detectors

Abstract

We present PRINCESS, a computational tool designed to predict gravitational wave observations from compact binary coalescences (CBCs) in current and future detector networks. PRINCESS uniquely combines predictions of both individual gravitational wave events and the associated astrophysical background, leveraging user-provided CBC catalogs. With the anticipated improvements in detector sensitivity from second-generation (2G) to third-generation (3G) observatories like the Einstein Telescope and Cosmic Explorer, the tool aims to constrain models of stellar formation and compact object evolution. PRINCESS calculates the signal-to-noise ratio (SNR) for individual events and predicts the stochastic background arising from unresolved sources. We detail the code structure, installation, and usage, providing examples of predictions for different astrophysical models and detector configurations. Results include forecasts for binary black hole detections and background spectra, highlighting the potential of future networks to resolve nearly all CBC events. We also discuss ongoing developments to expand PRINCESS's capabilities, including accounting for additional sources such as extreme mass ratio inspirals (EMRIs) and incorporating more advanced detector models.