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Abstract Pulsar timing is a process of iteratively fitting pulse arrival times to constrain the spindown, astrometric, and possibly binary parameters of a pulsar, by enforcing integer numbers of pulsar rotations between the arrival times. Phase connection is the process of unambiguously determining those rotation numbers between the times of arrival while determining a pulsar timing solution. Pulsar timing currently requires a manual process of step-by-step phase connection performed by individuals. In an effort to quantify and streamline this process, we created the Algorithmic Pulsar Timer (APT), an algorithm that can accurately phase connect and time isolate pulsars. Using the statistical F-test and knowledge of parameter uncertainties and covariances, the algorithm decides what new data to include in a fit, when to add additional timing parameters, and which model to attempt in subsequent iterations. Using these tools, the algorithm can phase-connect timing data that previously required substantial manual effort. We tested the algorithm on 100 simulated systems, with a 99% success rate. APT combines statistical tests and techniques with a logical decision-making process, very similar to the manual one used by pulsar astronomers for decades, and some computational brute force, to automate the often tricky process of isolated pulsar phase connection, setting the foundation for automated fitting of binary pulsar systems. 

The detection of starlight from the host galaxies of quasars during the reionization epoch (z > 6) has been elusive, even with deep HST observations1,2. The current highest redshift quasar host detected3, at z = 4.5, required the magnifying effect of a foreground lensing galaxy. Low-luminosity quasars4,5,6 from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP)7 mitigate the challenge of detecting their underlying, previously-undetected host galaxies. Here we report rest-frame optical images and spectroscopy of two HSC-SSP quasars at z > 6 with JWST. Using NIRCam imaging at 3.6μm and 1.5μm and subtracting the light from the unresolved quasars, we find that the host galaxies are massive (stellar masses of 13 × and 3.4 × 1010 M⊙, respectively), compact, and disk-like. NIRSpec medium-resolution spectroscopy shows stellar absorption lines in the more massive quasar, confirming the detection of the host. Velocity-broadened gas in the vicinity of these quasars enables measurements of their black hole masses (1.4 × 109 and 2.0 × 108 M⊙, respectively). Their location in the black hole mass - stellar mass plane is consistent with the distribution at low redshift, suggesting that the relation between black holes and their host galaxies was already in place less than a billion years after the Big Bang. 

Abstract Over the past few decades, the measurement precision of some pulsar timing experiments has advanced from ∼10 μ s to ∼10 ns, revealing many subtle phenomena. Such high precision demands both careful data handling and sophisticated timing models to avoid systematic error. To achieve these goals, we present PINT ( P INT I s N ot T empo3 ), a high-precision Python pulsar timing data analysis package, which is hosted on GitHub and available on the Python Package Index (PyPI) as pint-pulsar . PINT is well tested, validated, object oriented, and modular, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. It utilizes well-debugged public Python packages (e.g., the N um P y and A stropy libraries) and modern software development schemes (e.g., version control and efficient development with git and GitHub) and a continually expanding test suite for improved reliability, accuracy, and reproducibility. PINT is developed and implemented without referring to, copying, or transcribing the code from other traditional pulsar timing software packages (e.g., Tempo / Tempo2 ) and therefore provides a robust tool for cross-checking timing analyses and simulating pulse arrival times. In this paper, we describe the design, use, and validation of PINT , and we compare timing results between it and Tempo and Tempo2 .