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  1. Abstract

    We present a population of 11 of the faintest (>25.5 AB mag) short gamma-ray burst (GRB) host galaxies. We model their sparse available observations using the stellar population inference codeProspector-βand develop a novel implementation to incorporate the galaxy mass–radius relation. Assuming these hosts are randomly drawn from the galaxy population and conditioning this draw on their observed flux and size in a few photometric bands, we determine that these hosts have dwarf galaxy stellar masses of7.0log(M*/M)9.1. This is striking as only 14% of short GRB hosts with previous inferred stellar masses hadM*≲ 109M. We further show these short GRBs have smaller physical and host-normalized offsets than the rest of the population, suggesting that the majority of their neutron star (NS) merger progenitors were retained within their hosts. The presumably shallow potentials of these hosts translate to small escape velocities of ∼5.5–80 km s−1, indicative of either low postsupernova systemic velocities or short inspiral times. While short GRBs with identified dwarf host galaxies now comprise ≈14% of the total Swift-detected population, a number are likely missing in the current population, as larger systemic velocities (observed from the Galactic NS population) would result in highly offset short GRBs and less secure host associations. However, the revelation of a population of short GRBs retained in low-mass host galaxies offers a natural explanation for the observedr-process enrichment via NS mergers in Local Group dwarf galaxies, and has implications for gravitational-wave follow-up strategies.

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  2. Abstract

    One of the goals of gravitational-wave astrophysics is to infer the number and properties of the formation channels of binary black holes (BBHs); to do so, one must be able to connect various models with the data. We explore benefits and potential issues with analyses using models informed by population synthesis. We consider five possible formation channels of BBHs, as in Zevin et al. (2021b). First, we confirm with the GWTC-3 catalog what Zevin et al. (2021b) found in the GWTC-2 catalog, i.e., that the data are not consistent with the totality of observed BBHs forming in any single channel. Next, using simulated detections, we show that the uncertainties in the estimation of the branching ratios can shrink by up to a factor of ∼1.7 as the catalog size increases from 50 to 250, within the expected number of BBH detections in LIGO–Virgo–KAGRA's fourth observing run. Finally, we show that this type of analysis is prone to significant biases. By simulating universes where all sources originate from a single channel, we show that the influence of the Bayesian prior can make it challenging to conclude that one channel produces all signals. Furthermore, by simulating universes where all five channels contribute but only a subset of channels are used in the analysis, we show that biases in the branching ratios can be as large as ∼50% with 250 detections. This suggests that caution should be used when interpreting the results of analyses based on strongly modeled astrophysical subpopulations.

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  3. Abstract

    The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

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  4. Abstract

    Several features in the mass spectrum of merging binary black holes (BBHs) have been identified using data from the Third Gravitational Wave Transient Catalog (GWTC-3). These features are of particular interest as they may encode the uncertain mechanism of BBH formation. We assess if the features are statistically significant or the result of Poisson noise due to the finite number of observed events. We simulate catalogs of BBHs whose underlying distribution does not have the features of interest, apply the analysis previously performed on GWTC-3, and determine how often such features are spuriously found. We find that one of the features found in GWTC-3, the peak at ∼35M, cannot be explained by Poisson noise alone: peaks as significant occur in 1.7% of catalogs generated from a featureless population. This peak is therefore likely to be of astrophysical origin. The data is suggestive of an additional significant peak at ∼10M, though the exact location of this feature is not resolvable with current observations. Additional structure beyond a power law, such as the purported dip at ∼14M, can be explained by Poisson noise. We also provide a publicly available package,GWMockCat, that creates simulated catalogs of BBH events with correlated measurement uncertainty and selection effects according to user-specified underlying distributions and detector sensitivities.

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  5. Abstract

    There are few observed high-mass X-ray binaries (HMXBs) that harbor massive black holes (BHs), and none are likely to result in a binary black hole (BBH) that merges within a Hubble time; however, we know that massive merging BBHs exist from gravitational-wave (GW) observations. We investigate the role that X-ray and GW observational selection effects play in determining the properties of their respective detected binary populations. We find that, as a result of selection effects, detectable HMXBs and detectable BBHs form at different redshifts and metallicities, with detectable HMXBs forming at much lower redshifts and higher metallicities than detectable BBHs. We also find disparities in the mass distributions of these populations, with detectable merging BBH progenitors pulling to higher component masses relative to the full detectable HMXB population. Fewer than 3% of detectable HMXBs host BHs >35Min our simulated populations. Furthermore, we find the probability that a detectable HMXB will merge as a BBH system within a Hubble time is ≃0.6%. Thus, it is unsurprising that no currently observed HMXB is predicted to form a merging BBH with high probability.

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  6. Abstract The population of binary black hole mergers identified through gravitational waves has uncovered unexpected features in the intrinsic properties of black holes in the universe. One particularly surprising and exciting result is the possible existence of black holes in the pair-instability mass gap, ∼50–120 M ⊙ . Dense stellar environments can populate this region of mass space through hierarchical mergers, with the retention efficiency of black hole merger products strongly dependent on the escape velocity of the host environment. We use simple toy models to represent hierarchical merger scenarios in various dynamical environments. We find that hierarchical mergers in environments with high escape velocities (≳300 km s −1 ) are efficiently retained. If such environments dominate the binary black hole merger rate, this would lead to an abundance of high-mass mergers that is potentially incompatible with the empirical mass spectrum from the current catalog of binary black hole mergers. Models that efficiently generate hierarchical mergers, and contribute significantly to the observed population, must therefore be tuned to avoid a “cluster catastrophe” of overproducing binary black hole mergers within and above the pair-instability mass gap. 
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  7. Abstract The delay time distribution of neutron star mergers provides critical insights into binary evolution processes and the merger rate evolution of compact object binaries. However, current observational constraints on this delay time distribution rely on the small sample of Galactic double neutron stars (with uncertain selection effects), a single multimessenger gravitational wave event, and indirect evidence of neutron star mergers based on r -process enrichment. We use a sample of 68 host galaxies of short gamma-ray bursts to place novel constraints on the delay time distribution and leverage this result to infer the merger rate evolution of compact object binaries containing neutron stars. We recover a power-law slope of α = − 1.83 − 0.39 + 0.35 (median and 90% credible interval) with α < −1.31 at 99% credibility, a minimum delay time of t min = 184 − 79 + 67 Myr with t min > 72 Myr at 99% credibility, and a maximum delay time constrained to t max > 7.95 Gyr at 99% credibility. We find these constraints to be broadly consistent with theoretical expectations, although our recovered power-law slope is substantially steeper than the conventional value of α = −1, and our minimum delay time is larger than the typically assumed value of 10 Myr. Pairing this cosmological probe of the fate of compact object binary systems with the Galactic population of double neutron stars will be crucial for understanding the unique selection effects governing both of these populations. In addition to probing a significantly larger redshift regime of neutron star mergers than possible with current gravitational wave detectors, complementing our results with future multimessenger gravitational wave events will also help determine if short gamma-ray bursts ubiquitously result from compact object binary mergers. 
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  8. Abstract

    The existence of black holes (BHs) with masses in the range between stellar remnants and supermassive BHs has only recently become unambiguously established. GW190521, a gravitational wave signal detected by the LIGO/Virgo Collaboration, provides the first direct evidence for the existence of such intermediate-mass BHs (IMBHs). This event sparked and continues to fuel discussion on the possible formation channels for such massive BHs. As the detection revealed, IMBHs can form via binary mergers of BHs in the “upper mass gap” (≈40–120M). Alternatively, IMBHs may form via the collapse of a very massive star formed through stellar collisions and mergers in dense star clusters. In this study, we explore the formation of IMBHs with masses between 120 and 500Min young, massive star clusters using state-of-the-art Cluster Monte Carlo models. We examine the evolution of IMBHs throughout their dynamical lifetimes, ending with their ejection from the parent cluster due to gravitational radiation recoil from BH mergers, or dynamical recoil kicks from few-body scattering encounters. We find thatallof the IMBHs in our models are ejected from the host cluster within the first ∼500 Myr, indicating a low retention probability of IMBHs in this mass range for globular clusters today. We estimate the peak IMBH merger rate to be2Gpc3yr1at redshiftz≈ 2.

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  9. Abstract Orbital eccentricity is one of the most robust discriminators for distinguishing between dynamical and isolated formation scenarios of binary black hole mergers using gravitational-wave observatories such as LIGO and Virgo. Using state-of-the-art cluster models, we show how selection effects impact the detectable distribution of eccentric mergers from clusters. We show that the observation (or lack thereof) of eccentric binary black hole mergers can significantly constrain the fraction of detectable systems that originate from dynamical environments, such as dense star clusters. After roughly 150 observations, observing no eccentric binary signals would indicate that clusters cannot make up the majority of the merging binary black hole population in the local universe (95% credibility). However, if dense star clusters dominate the rate of eccentric mergers and a single system is confirmed to be measurably eccentric in the first and second gravitational-wave transient catalogs, clusters must account for at least 14% of detectable binary black hole mergers. The constraints on the fraction of detectable systems from dense star clusters become significantly tighter as the number of eccentric observations grows and will be constrained to within 0.5 dex once 10 eccentric binary black holes are observed. 
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  10. 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. 

    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: 

    1. A glitches must be classified by at least 2 registered volunteers
    2. 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
    3. 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

    The choice of these and other parameterization will affect the accuracy of the retired dataset as well as the number of glitches that are retired, and will be explored in detail in an upcoming publication (Zevin et al. in prep). 

    The dataset can be read in using e.g. Pandas: 
    import pandas as pd
    dataset = pd.read_hdf('retired_fulldata_min2_max50_ret0p9.hdf5', key='image_db')
    Each row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. 

    Description of series in dataframe

    • ['1080Lines', '1400Ripples', 'Air_Compressor', 'Blip', 'Chirp', 'Extremely_Loud', 'Helix', 'Koi_Fish', 'Light_Modulation', 'Low_Frequency_Burst', 'Low_Frequency_Lines', 'No_Glitch', 'None_of_the_Above', 'Paired_Doves', 'Power_Line', 'Repeating_Blips', 'Scattered_Light', 'Scratchy', 'Tomte', 'Violin_Mode', 'Wandering_Line', 'Whistle']
      • Machine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity
    • ['ml_confidence', 'ml_label']
      • Highest machine learning confidence score across all classes for a particular glitch, and the class associated with this score
    • ['gravityspy_id', 'id']
      • Unique identified for each glitch on the Zooniverse platform ('gravityspy_id') and in the Gravity Spy project ('id'), which can be used to link a particular glitch to the full Gravity Spy dataset (which contains GPS times among many other descriptors)
    • ['retired']
      • Marks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)
    • ['Nclassifications']
      • The total number of classifications performed by registered volunteers on this glitch
    • ['final_score', 'final_label']
      • The final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch
    • ['tracks']
      • Array of classification weights that were added to each glitch category due to each volunteer's classification


    For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo

    For the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.

    For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. 

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