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  1. Abstract

    Gravitational-wave observations of binary black hole (BBH) systems point to black hole spin magnitudes being relatively low. These measurements appear in tension with high spin measurements for high-mass X-ray binaries (HMXBs). We use grids of MESA simulations combined with the rapid population-synthesis code COSMIC to examine the origin of these two binary populations. It has been suggested that Case-A mass transfer while both stars are on the main sequence can form high-spin BHs in HMXBs. Assuming this formation channel, we show that depending on the critical mass ratios for the stability of mass transfer, 48%–100% of these Case-A HMXBs merge during the common-envelope phase and up to 42% result in binaries too wide to merge within a Hubble time. Both MESA and COSMIC show that high-spin HMXBs formed through Case-A mass transfer can only form merging BBHs within a small parameter space where mass transfer can lead to enough orbital shrinkage to merge within a Hubble time. We find that only up to 11% of these Case-A HMXBs result in BBH mergers, and at most 20% of BBH mergers came from Case-A HMXBs. Therefore, it is not surprising that these two spin distributions are observed to be different.

  2. Abstract

    Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observations. Binary population synthesis with full simulation of stellar structure and evolution is computationally expensive, requiring a large number of mass-transfer sequences. The recently developed binary population synthesis codePOSYDONincorporates grids ofMESAbinary star simulations that are interpolated to model large-scale populations of massive binaries. The traditional method of computing a high-density rectilinear grid of simulations is not scalable for higher-dimension grids, accounting for a range of metallicities, rotation, and eccentricity. We present a new active learning algorithm,psy-cris, which uses machine learning in the data-gathering process to adaptively and iteratively target simulations to run, resulting in a custom, high-performance training set. We testpsy-crison a toy problem and find the resulting training sets require fewer simulations for accurate classification and regression than either regular or randomly sampled grids. We further applypsy-cristo the target problem of building a dynamic grid ofMESAsimulations, and we demonstrate that, even without fine tuning, a simulation set of only ∼1/4 the size of a rectilinear grid is sufficient to achieve the same classification accuracy. We anticipate further gains when algorithmic parameters are optimized for the targeted application. We find that optimizing for classificationmore »only may lead to performance losses in regression, and vice versa. Lowering the computational cost of producing grids will enable new population synthesis codes such asPOSYDONto cover more input parameters while preserving interpolation accuracies.

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  3. Abstract The science objectives of the LISA mission have been defined under the implicit assumption of a 4-years continuous data stream. Based on the performance of LISA Pathfinder, it is now expected that LISA will have a duty cycle of $$\approx 0.75$$ ≈ 0.75 , which would reduce the effective span of usable data to 3 years. This paper reports the results of a study by the LISA Science Group, which was charged with assessing the additional science return of increasing the mission lifetime. We explore various observational scenarios to assess the impact of mission duration on the main science objectives of the mission. We find that the science investigations most affected by mission duration concern the search for seed black holes at cosmic dawn, as well as the study of stellar-origin black holes and of their formation channels via multi-band and multi-messenger observations. We conclude that an extension to 6 years of mission operations is recommended.