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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. 

{"Abstract":["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>\n\nWhen 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>\n\nA glitches must be classified by at least 2 registered volunteers<\/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>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>\n\nThe 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). <\/p>\n\nThe dataset can be read in using e.g. Pandas: \n```\nimport pandas as pd\ndataset = pd.read_hdf('retired_fulldata_min2_max50_ret0p9.hdf5', key='image_db')\n```\nEach row in the dataframe contains information about a particular glitch in the Gravity Spy dataset. <\/p>\n\nDescription of series in dataframe<\/strong><\/p>\n\n['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']\n\tMachine learning scores for each glitch class in the trained model, which for a particular glitch will sum to unity<\/li><\/ul>\n\t<\/li>['ml_confidence', 'ml_label']\n\tHighest machine learning confidence score across all classes for a particular glitch, and the class associated with this score<\/li><\/ul>\n\t<\/li>['gravityspy_id', 'id']\n\tUnique 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)<\/li><\/ul>\n\t<\/li>['retired']\n\tMarks whether the glitch is retired using our default set of retirement parameters (1=retired, 0=not retired)<\/li><\/ul>\n\t<\/li>['Nclassifications']\n\tThe total number of classifications performed by registered volunteers on this glitch<\/li><\/ul>\n\t<\/li>['final_score', 'final_label']\n\tThe final score (weighted combination of machine learning and volunteer classifications) and the most probable type of glitch<\/li><\/ul>\n\t<\/li>['tracks']\n\tArray of classification weights that were added to each glitch category due to each volunteer's classification<\/li><\/ul>\n\t<\/li><\/ul>\n\n <\/p>\n\n```\nFor machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo<\/p>\n\nFor the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.<\/p>\n\nFor 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. <\/p>"]} 

Abstract Kilonovae are ultraviolet, optical, and infrared transients powered by the radioactive decay of heavy elements following a neutron star merger. Joint observations of kilonovae and gravitational waves can offer key constraints on the source of Galacticr-process enrichment, among other astrophysical topics. However, robust constraints on heavy element production require rapid kilonova detection (within ∼1 day of merger) as well as multiwavelength observations across multiple epochs. In this study, we quantify the ability of 13 wide-field-of-view instruments to detect kilonovae, leveraging a large grid of over 900 radiative transfer simulations with 54 viewing angles per simulation. We consider both current and upcoming instruments, collectively spanning the full kilonova spectrum. The Roman Space Telescope has the highest redshift reach of any instrument in the study, observing kilonovae out toz∼ 1 within the first day post-merger. We demonstrate that BlackGEM, DECam, GOTO, the Vera C. Rubin Observatory’s LSST, ULTRASAT, VISTA, and WINTER can observe some kilonovae out toz∼ 0.1 (∼475 Mpc), while DDOTI, MeerLICHT, PRIME, Swift/UVOT, and ZTF are confined to more nearby observations. Furthermore, we provide a framework to infer kilonova ejecta properties following nondetections and explore variation in detectability with these ejecta parameters.