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Abstract It has been proposed that some black holes (BHs) in binary black hole (BBH) systems are born from “hierarchical mergers” (HMs), i.e., earlier mergers of smaller BHs. These HM products have spin magnitudes χ ∼ 0.7, and, if they are dynamically assembled into BBH systems, their spin orientations will sometimes be antialigned with the binary orbital angular momentum. In fact, as Baibhav et al. showed, ∼16% of BBH systems that include HM products will have an effective inspiral spin parameter, χ eff < −0.3. Nevertheless, the LIGO–Virgo–KAGRA (LVK) gravitationalwave (GW) detectors have yet to observe a BBH system with χ eff ≲ −0.2, leading to upper limits on the fraction of HM products in the population. We fit the astrophysical mass and spin distribution of BBH systems and measure the fraction of BBH systems with χ eff < −0.3, which implies an upper limit on the HM fraction. We find that fewer than 26% of systems in the underlying BBH population include HM products (90% credibility). Even among BBH systems with primary masses m 1 = 60 M ⊙ , the HM fraction is less than 69%, which may constrain the location of the pairinstability mass gap. With 300more »Free, publiclyaccessible full text available August 1, 2023

Abstract Gravitationalwave 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 highmass Xray binaries (HMXBs). We use grids of MESA simulations combined with the rapid populationsynthesis code COSMIC to examine the origin of these two binary populations. It has been suggested that CaseA mass transfer while both stars are on the main sequence can form highspin 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 CaseA HMXBs merge during the commonenvelope phase and up to 42% result in binaries too wide to merge within a Hubble time. Both MESA and COSMIC show that highspin HMXBs formed through CaseA 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 CaseA HMXBs result in BBH mergers, and at most 20% of BBH mergers came from CaseA HMXBs. Therefore, it is not surprising that these two spin distributions are observed to be different.

Abstract The component black holes (BHs) observed in gravitationalwave (GW) binary black hole (BBH) events tend to be more massive and slower spinning than those observed in black hole Xray binaries (BHXRBs). Without modeling their evolutionary histories, we investigate whether these apparent tensions in the BH populations can be explained by GW observational selection effects alone. We find that this is indeed the case for the discrepancy between BH masses in BBHs and the observed highmass Xray binaries (HMXBs), when we account for statistical uncertainty from the small sample size of just three HMXBs. On the other hand, the BHs in observed lowmass Xray binaries (LMXBs) are significantly lighter than the astrophysical BBH population, but this may just be due to a correlation between component masses in a binary system. Given their light stellar companions, we expect light BHs in LMXBs. The observed spins in HMXBs and LMXBs, however, are in tension with the inferred BBH spin distribution at the >99.9% level. We discuss possible scenarios behind the significantly larger spins in observed BHXRBs. One possibility is that a small subpopulation (conservatively <30%) of BBHs have rapidly spinning primary components, indicating that they may have followed a similar evolutionary pathwaymore »Free, publiclyaccessible full text available April 1, 2023

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 masstransfer sequences. The recently developed binary population synthesis code
POSYDON incorporates grids ofMESA binary star simulations that are interpolated to model largescale populations of massive binaries. The traditional method of computing a highdensity rectilinear grid of simulations is not scalable for higherdimension grids, accounting for a range of metallicities, rotation, and eccentricity. We present a new active learning algorithm,psycris , which uses machine learning in the datagathering process to adaptively and iteratively target simulations to run, resulting in a custom, highperformance training set. We testpsycris on 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 applypsycris to the target problem of building a dynamic grid ofMESA simulations, 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 » 
Abstract
<p>This dataset contains machine learning and volunteer classifications from the Gravity Spy project. It includes glitches from observing runs O1, O2, O3a and O3b that received at least one classification from a registered volunteer in the project. It also indicates glitches that are nominally retired from the project using our default set of retirement parameters, which are described below. See more details in the Gravity Spy Methods paper. </p> <p>When a particular subject in a citizen science project (in this case, glitches from the LIGO datastream) is deemed to be classified sufficiently it is "retired" from the project. For the Gravity Spy project, retirement depends on a combination of both volunteer and machine learning classifications, and a number of parameterizations affect how quickly glitches get retired. For this dataset, we use a default set of retirement parameters, the most important of which are: </p> <ol><li>A glitches must be classified by at least 2 registered volunteers</li><li>Based on both the initial machine learning classification and volunteer classifications, the glitch has more than a 90% probability of residing in a particular class</li><li>Each volunteer classification (weighted by that volunteer's confusion matrix) contains a weight equal to the initial machine learning score when determining the final probability</li></ol> <p>The choice of these and other