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Abstract Pulsar timing arrays (PTAs) are sensitive to low-frequency gravitational waves (GWs), which induce correlated changes in millisecond pulsars’ timing residuals. PTA collaborations around the world have recently announced evidence of a nanohertz gravitational wave background (GWB), which may be produced by a population of supermassive black hole binaries (SMBHBs). The GWB is often modeled as following a power-law power spectral density (PSD); however, a GWB produced by a cosmological population of SMBHBs is expected to have a more complex power spectrum due to the discrete nature of the sources. In this paper, we investigate using at-process PSD to model the GWB, which allows us to fit for both the underlying power-law amplitude and spectral index as well as deviations from that power law, which may be produced by individual nearby binaries. We create simulated data sets based on the properties of the NANOGrav 15 yr data set, and we demonstrate that thet-process PSD can accurately recover the PSD when deviations from a power law are present. With longer timed data sets and more pulsars, we expect the sensitivity of our PTAs to improve, which will allow us to precisely measure the PSD of the GWB and study the sources producing it. 

Abstract While supermassive black hole (SMBH) binaries are not the only viable source for the low-frequency gravitational wave background (GWB) signal evidenced by the most recent pulsar timing array (PTA) data sets, they are expected to be the most likely. Thus, connecting the measured PTA GWB spectrum and the underlying physics governing the demographics and dynamics of SMBH binaries is extremely important. Previously, Gaussian processes (GPs) and dense neural networks have been used to make such a connection by being built as conditional emulators; their input is some selected evolution or environmental SMBH binary parameters and their output is the emulated mean and standard deviation of the GWB strain ensemble distribution over many Universes. In this paper, we use a normalizing flow (NF) emulator that is trained on the entirety of the GWB strain ensemble distribution, rather than only mean and standard deviation. As a result, we can predict strain distributions that mirror underlying simulations very closely while also capturing frequency covariances in the strain distributions as well as statistical complexities such as tails, non-Gaussianities, and multimodalities that are otherwise not learnable by existing techniques. In particular, we feature various comparisons between the NF-based emulator and the GP approach used extensively in past efforts. Our analyses conclude that the NF-based emulator not only outperforms GPs in the ease and computational cost of training but also outperforms in the fidelity of the emulated GWB strain ensemble distributions. 

Abstract Supermassive black holes (SMBHs) reside at the center of every massive galaxy in the local universe with masses that closely correlate with observations of their host galaxy, implying a connected evolutionary history. The population of binary SMBHs, which form following galaxy mergers, is expected to produce a gravitational-wave background (GWB) detectable by pulsar timing arrays (PTAs). PTAs are starting to see hints of what may be a GWB, and the amplitude of the emerging signal is toward the higher end of model predictions. Simulated populations of binary SMBHs can be constructed from observations of galaxies and are used to make predictions about the nature of the GWB. The greatest source of uncertainty in these observation-based models comes from the inference of the SMBH mass function, which is derived from observed host galaxy properties. In this paper, I undertake a new approach for inferring the SMBH mass function, starting from a velocity dispersion function rather than a galaxy stellar mass function. I argue that this method allows for a more direct inference by relying on a larger suite of individual galaxy observations as well as relying on a more “fundamental” SMBH mass relation. I find that the resulting binary SMBH population contains more massive systems at higher redshifts than previous models. Additionally, I explore the implications for the detection of individually resolvable sources in PTA data. 

ABSTRACT We examine the effect of supermassive black hole (SMBH) mass scaling relation choice on the inferred SMBH mass population since redshift z ∼ 3. To make robust predictions for the gravitational wave background (GWB), we must have a solid understanding of the underlying SMBH demographics. Using the SDSS and 3D-HST + CANDELS surveys for 0 < z < 3, we evaluate the inferred SMBH masses from two SMBH–galaxy scaling relations: MBH–Mbulge and MBH–σ. Our SMBH mass functions come directly from stellar mass measurements for MBH–Mbulge, and indirectly from stellar mass and galaxy radius measurements along with the galaxy mass fundamental plane for MBH–σ. We find that there is a substantial difference in predictions especially for z > 1, and this difference increases out to z = 3. In particular, we find that using velocity dispersion predicts a greater number of SMBHs with masses greater than 109 M⊙. The GWB that pulsar timing arrays find evidence for is higher in amplitude than expected from GWB predictions which rely on high-redshift extrapolations of local SMBH mass–galaxy scaling relations. The difference in SMBH demographics resulting from different scaling relations may be the origin for the mismatch between the signal amplitude and predictions. Generally, our results suggest that a deeper understanding of the potential redshift evolution of these relations is needed if we are to draw significant insight from their predictions at z > 1. 

Abstract Pulsar timing array observations have found evidence for an isotropic gravitational-wave background with the Hellings–Downs angular correlations between pulsar pairs. This interpretation hinges on the measured shape of the angular correlations, which is predominantly quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15 yr pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipolesℓ≥ 2, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and nonzero at the ∼95% confidence level and a Bayes factor of 200. When we include multipolesℓ≤ 1, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz, which has also been noted in previous analyses of the NANOGrav 15 yr data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations. 

Abstract The identification of bright quasars atz≳ 6 enables detailed studies of supermassive black holes, massive galaxies, structure formation, and the state of the intergalactic medium within the first billion years after the Big Bang. We present the spectroscopic confirmation of 55 quasars at redshifts 5.6 <z< 6.5 and UV magnitudes −24.5 <M₁₄₅₀< −28.5 identified in the optical Pan-STARRS1 and near-IR VIKING surveys (48 and 7, respectively). Five of these quasars have independently been discovered in other studies. The quasar sample shows an extensive range of physical properties, including 17 objects with weak emission lines, 10 broad absorption line quasars, and 5 objects with strong radio emission (radio-loud quasars). There are also a few notable sources in the sample, including a blazar candidate atz= 6.23, a likely gravitationally lensed quasar atz= 6.41, and az= 5.84 quasar in the outskirts of the nearby (D∼ 3 Mpc) spiral galaxy M81. The blazar candidate remains undetected in NOEMA observations of the [Cii]and underlying emission, implying a star formation rate <30–70M_⊙yr⁻¹. A significant fraction of the quasars presented here lies at the foundation of the first measurement of thez∼ 6 quasar luminosity function from Pan-STARRS1 (introduced in a companion paper). These quasars will enable further studies of the high-redshift quasar population with current and future facilities. 

Abstract With strong evidence of a common-spectrum stochastic process in the most recent data sets from the NANOGrav Collaboration, the European Pulsar Timing Array (PTA), Parkes PTA, and the International PTA, it is crucial to assess the effects of the several astrophysical and cosmological sources that could contribute to the stochastic gravitational wave background (GWB). Using the same data set creation and injection techniques as in Pol et al., we assess the separability of multiple GWBs by creating single and multiple GWB source data sets. We search for these injected sources using Bayesian PTA analysis techniques to assess recovery and separability of multiple astrophysical and cosmological backgrounds. For a GWB due to supermassive black hole binaries and an underlying weaker background due to primordial gravitational waves with a GW energy-density ratio of Ω_PGW/Ω_SMBHB= 0.5, the Bayes’ factor for a second process exceeds unity at 17 yr, and increases with additional data. At 20 yr of data, we are able to constrain the spectral index and amplitude of the weaker GWB at this density ratio to a fractional uncertainty of 64% and 110%, respectively, using current PTA methods and techniques. Using these methods and findings, we outline a basic protocol to search for multiple backgrounds in future PTA data sets. 

Abstract Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15 yr pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese pulsar timing arrays. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational-wave background (GWB). To explore the internal consistency of this result, we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure the correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic GWB in the NANOGrav 15 yr data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data.