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Abstract Wind Roche-lobe overflow (WRLOF) is a mass-transfer mechanism proposed by Mohamed and Podsiadlowski for stellar binaries wherein the wind acceleration zone of the donor star exceeds its Roche-lobe radius, allowing stellar wind material to be transferred to the accretor at enhanced rates. WRLOF may explain characteristics observed in blue lurkers and blue stragglers. While WRLOF has been implemented in rapid population synthesis codes, it has yet to be explored thoroughly in detailed binary models such asMESA(a 1D stellar evolution code), and over a wide range of initial binary configurations. We incorporate WRLOF accretion inMESAto investigate wide low-mass binaries at solar metallicity. We perform a parameter study over the initial orbital periods and stellar masses. In most of the models where we consider angular momentum transfer during accretion, the accretor is spun up to the critical (or breakup) rotation rate. Then we assume the star develops a boosted wind to efficiently reduce the angular momentum so that it could maintain subcritical rotation. Balanced by boosted wind loss, the accretor only gains ∼2% of its total mass, but can maintain a near-critical rotation rate during WRLOF. Notably, the mass-transfer efficiency is significantly smaller than in previous studies in which the rotation of the accretor is ignored. We compare our results to observational data of blue lurkers in M67 and find that the WRLOF mechanism can qualitatively explain the origin of their rapid rotation, their location on the H-R diagram, and their orbital periods. 

Abstract PINTis a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework withinPINTto characterize the single-pulsar noise processes present in pulsar timing data sets. This framework enables parameter estimation for both uncorrelated and correlated noise processes, as well as model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated data sets as well as a real data set of PSR B1855+09. We also describe the new features implemented inPINTsince it was first described in the literature.