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Abstract The formation histories of compact binary mergers, especially stellar-mass binary black hole mergers, have recently come under increased scrutiny and revision. We revisit the question of the dominant formation channel and efficiency of forming binary neutron star (BNS) mergers. We use the stellar and binary evolution codeMESAand implement a detailed method for common envelope and mass transfer. We perform simulations for donor masses between 7 M_⊙and 20 M_⊙with a neutron star (NS) companion of 1.4 M_⊙and 2.0 M_⊙ at two metallicities, using varying common envelope efficiencies and two different prescriptions to determine if the donor undergoes core collapse or electron capture, given their helium and carbon–oxygen cores. In contrast to the case of binary black hole mergers, for an NS companion of 1.4 M_⊙, all BNS mergers are formed following a common envelope phase. For an NS mass of 2.0 M_⊙, we identify a small subset of mergers following only stable mass transfer if the NS receives a natal kick sampled from a Maxwellian distribution with velocity dispersionσ= 265 km s⁻¹. Regardless of the supernova prescription, we find more BNS mergers at subsolar metallicity compared to solar. 

The LIGO-Virgo-KAGRA (LVK) Collaboration has made breakthrough discoveries in gravitational-wave astronomy, a new field that provides a different means of observing our Universe. Gravitational-wave discoveries are possible thanks to the work of thousands of people from across the globe working together. In this article, we discuss the range of engagement activities used to communicate LVK gravitational-wave discoveries and the stories of the people behind the science, using the activities surrounding the release of the third Gravitational-Wave Transient Catalog as a case study. 

This data set contains the individual classifications that the Gravity Spy citizen science volunteers made for glitches through 20 July 2024. Classifications made by science team members or in testing workflows have been removed as have classifications of glitches lacking a Gravity Spy identifier. See Zevin et al. (2017) for an explanation of the citizen science task and classification interface. Data about glitches with machine-learning labels are provided in an earlier data release (Glanzer et al., 2021). Final classifications combining ML and volunteer classifications are provided in Zevin et al. (2022).  22 of the classification labels match the labels used in the earlier data release, namely 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 and Whistle. One glitch class that was added to the machine-learning classification has not been added to the Zooniverse project and so does not appear in this file, namely Blip_Low_Frequency. Four classes were added to the citizen science platform but not to the machine learning model and so have only volunteer labels, namely 70HZLINE, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP and PIZZICATO. The glitch class Fast_Scattering added to the machine-learning classification has an equivalent volunteer label CROWN, which is used here (Soni et al. 2021). Glitches are presented to volunteers in a succession of workflows. Workflows include glitches classified by a machine learning classifier as being likely to be in a subset of classes and offer the option to classify only those classes plus None_of_the_Above. Each level includes the classes available in lower levels. The top level does not add new classification options but includes all glitches, including those for which the machine learning model is uncertain of the class. As the classes available to the volunteers change depending on the workflow, a glitch might be classified as None_of_the_Above in a lower workflow and subsequently as a different class in a higher workflow. Workflows and available classes are shown in the table below.  Workflow ID Name Number of glitch classes Glitches added 1610  Level 1 3 Blip, Whistle, None_of_the_Above 1934 Level 2 6 Koi_Fish, Power_Line, Violin_Mode 1935 Level 3 10 Chirp, Low_Frequency_Burst, No_Glitch, Scattered_Light 2360 Original level 4 22 1080Lines, 1400Ripples, Air_Compressor, Extremely_Loud, Helix, Light_Modulation, Low_Frequency_Lines, Paired_Doves, Repeating_Blips, Scratchy, Tomte, Wandering_Line 7765 New level 4 15 1080Lines, Extremely_Loud, Low_Frequency_Lines, Repeating_Blips, Scratchy 2117 Original level 5 22 No new glitch classes 7766 New level 5 27 1400Ripples, Air_Compressor, Paired_Doves, Tomte, Wandering_Line, 70HZLINE, CROWN, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP, PIZZICATO 7767 Level 6 27 No new glitch classes Description of data fields Classification_id: a unique identifier for the classification. A volunteer may choose multiple classes for a glitch when classifying, in which case there will be multiple rows with the same classification_id. Subject_id: a unique identifier for the glitch being classified. This field can be used to join the classification to data about the glitch from the prior data release.  User_hash: an anonymized identifier for the user making the classification or for anonymous users an identifier that can be used to track the user within a session but which may not persist across sessions.  Anonymous_user: True if the classification was made by a non-logged in user.  Workflow: The Gravity Spy workflow in which the classification was made.  Workflow_version: The version of the workflow. Timestamp: Timestamp for the classification.  Classification: Glitch class selected by the volunteer.  Related datasets For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo For classifications of glitches combining machine learning and volunteer classifications, please see Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b. For the 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.