The NANOGrav 15 yr Data Set: Evidence for a Gravitational-wave Background

Abstract

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings–Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of 1014, and this same model is favored over an uncorrelated common power-law spectrum model with Bayes factors of 200–1000, depending on spectral modeling choices. We have built a statistical background distribution for the latter Bayes factors using a method that removes interpulsar correlations from our data set, finding p = 10−3 (≈3σ) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of interpulsar correlations yields p = 5 × 10−5 to 1.9 × 10−4 (≈3.5σ–4σ). Assuming a fiducial f −2/3 characteristic strain spectrum, as appropriate for an ensemble of binary supermassive black hole inspirals, the strain amplitude is 2.4 − 0.6 + 0.7 × 10 − 15 (median + 90% credible interval) at a reference frequency of 1 yr−1. The inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from a population of supermassive black hole binaries, although more exotic cosmological and astrophysical sources cannot be excluded. The observation of Hellings–Downs correlations points to the gravitational-wave origin of this signal.

The NANOGrav 15 yr Data Set: Bayesian Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries

“…Interestingly, the frequency of this low-frequency candidate is consistent with the subdominant monopolar signature that was found along the HD process in Agazie et al (2023b). Thus we investigated if an individual binary could account for that monopolar signature by calculating signal-to-noise ratio (S/N) Figure 1.…”

“…Three such pulsar timing arrays (PTAs) have the required decade-long data sets to probe the nanohertz band: the North American Nanohertz Observatory for Gravitational Waves (NANOGrav; Agazie et al 2023a), the European Pulsar Timing Array (EPTA; Antoniadis et al 2023a), and the Parkes Pulsar Timing Array (PPTA; Zic et al 2023). All three of these collaborations found a low-frequency stochastic process in their data (Agazie et al 2023b;Antoniadis et al 2023b;Reardon et al 2023). They also found various levels of evidence for Hellings-Downs (HD;Hellings & Downs 1983) spatial correlations between pulsars, which points to the origin of this process being a stochastic gravitational-wave background (GWB).…”

“…We analyze the NANOGrav 15 yr data set 67 , which consists of times-of-arrival and timing models of 68 millisecond-pulsars based on observations made between 2004 July and 2020 August. Similar to Agazie et al (2023b), we only analyze data from pulsars that have a timing baseline longer than 3 yr. This results in the exclusion of PSR J0614−3329 and brings the total number of analyzed pulsars to 67.…”

“…The common red noise process was shown to have correlations between pulsars that follow HD correlations (Agazie et al 2023b), so for a fully consistent modeling, we should include those correlations when searching for an individual binary. However, it is currently computationally prohibitive to directly model those correlations while also searching for signals from individual binaries.…”

“…We find that the presence of an individual binary with f GW ∼ 4 nHz is mildly favored over only having a CURN process (Bayes factor∼3). However, since Agazie et al (2023b) found evidence for the red noise process having HD correlations (HD versus CURN Bayes factor ∼200), the right models to compare are an HD-correlated background versus an HD-correlated background and an individual binary. We used methods outlined in Section 2.5 to take the correlations into account by re-weighing the posterior samples from an analysis including uncorrelated red noise.…”