The NANOGrav 15 yr Data Set: Piecewise Power-Law Reconstruction of the Gravitational-Wave Background

TL;DR

This paper introduces a flexible piecewise power-law method to accurately reconstruct the gravitational-wave background spectrum from NANOGrav's 15-year data, aiding in identifying its cosmic or astrophysical origins.

Contribution

It presents a novel, adaptable spectral reconstruction technique using piecewise power-laws, improving upon previous models for analyzing gravitational-wave background data.

Findings

Reconstructed the GWB spectrum with higher flexibility than standard models.

Provided data products enabling rapid refits of spectral GWB models.

Enhanced the physical realism of spectral analysis for cosmological signals.

Abstract

The NANOGrav 15-year (NG15) data set provides evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies, which is expected to originate either from a cosmic population of inspiraling supermassive black-hole binaries or new particle physics in the early Universe. A firm identification of the source of the NG15 signal requires an accurate reconstruction of its frequency spectrum. In this paper, we provide such a spectral characterization of the NG15 signal based on a piecewise power-law (PPL) ansatz that strikes a balance between existing alternatives in the literature. Our PPL reconstruction is more flexible than the standard constant-power-law model, which describes the GWB spectrum in terms of only two parameters: an amplitude A and a spectral index gamma. Concurrently, it better approximates physically realistic GWB spectra -- especially those of cosmological origin -- than the free spectral model, since the latter allows for arbitrary variations in the GWB amplitude from one frequency bin to the next. Our PPL reconstruction of the NG15 signal relies on individual PPL models with a fixed number of internal nodes (i.e., constant power law, broken power law, doubly broken power law, etc.) that are ultimately combined in a Bayesian model average. The data products resulting from our analysis provide the basis for fast refits of spectral GWB models.