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.
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.
more » « less- NSF-PAR ID:
- 10397990
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Classical and Quantum Gravity
- Volume:
- 40
- Issue:
- 6
- ISSN:
- 0264-9381
- Page Range / eLocation ID:
- Article No. 065004
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (O1, O2 and O3, where O3 is split into O3a and O3b). Gravity Spy classified all noise events identified by the Omicron trigger pipeline in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made Omega scans of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.
There are 22 classes used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are two additional classes used to classify O3 data.
For O1 and O2, the glitch classes were: 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
For O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Blip_Low_Frequency, Chirp, Extremely_Loud, Fast_Scattering, 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
If you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool GWpy. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command
```conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy```
After downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.
```
from gwpy.table import GravitySpyTable
H1_O2 = GravitySpyTable.read('H1_O2.csv')
H1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)]
H1_O2[0:4].download(nproc=1)
```
Each of the columns in the CSV files are taken from various different inputs:
[‘event_time’, ‘ifo’, ‘peak_time’, ‘peak_time_ns’, ‘start_time’, ‘start_time_ns’, ‘duration’, ‘peak_frequency’, ‘central_freq’, ‘bandwidth’, ‘channel’, ‘amplitude’, ‘snr’, ‘q_value’] contain metadata about the signal from the Omicron pipeline.
[‘gravityspy_id’] is the unique identifier for each glitch in the dataset.
[‘1400Ripples’, ‘1080Lines’, ‘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’] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity).
[‘ml_label’, ‘ml_confidence’] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification.
[‘url1’, ‘url2’, ‘url3’, ‘url4’] are the links to the publicly-available Omega scans for each glitch. ‘url1’ shows the glitch for a duration of 0.5 seconds, ‘url2’ for 1 seconds, ‘url3’ for 2 seconds, and ‘url4’ for 4 seconds.
```
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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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:
- A glitches must be classified by at least 2 registered volunteers
- 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
- 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 ZenodoFor 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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Abstract Transient signals of instrumental and environmental origins (‘glitches’) in gravitational wave data elevate the false alarm rate of searches for astrophysical signals and reduce their sensitivity. Glitches that directly overlap astrophysical signals hinder their detection and worsen parameter estimation errors. As the fraction of data occupied by detectable astrophysical signals will be higher in next generation detectors, such problematic overlaps could become more frequent. These adverse effects of glitches can be mitigated by estimating and subtracting them out from the data, but their unpredictable waveforms and large morphological diversity pose a challenge. Subtraction of glitches using data from auxiliary sensors as predictors works but not for the majority of cases. Thus, there is a need for nonparametric glitch mitigation methods that do not require auxiliary data, work for a large variety of glitches, and have minimal effect on astrophysical signals in the case of overlaps. In order to cope with the high rate of glitches, it is also desirable that such methods be computationally fast. We show that adaptive spline fitting, in which the placement of free knots is optimized to estimate both smooth and non-smooth curves in noisy data, offers a promising approach to satisfying these requirements for broadband short-duration glitches, the type that appear quite frequently. The method is demonstrated on glitches drawn from three distinct classes in the Gravity Spy database as well as on the glitch that overlapped the binary neutron star signal GW170817. The impact of glitch subtraction on the GW170817 signal, or those like it injected into the data, is seen to be negligible.
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Abstract We present a computational method to identify glitches in gravitational-wave data that occur nearby gravitational-wave signals from compact binary coalescences. The Q-transform, an established tool in LIGO-Virgo-KAGRA data analysis, computes the probability of any excess in the data surrounding a signal against the assumption of a Gaussian noise background, flagging any significant glitches. Subsequently, we perform validation tests on this computational method to ensure self-consistency in colored Gaussian noise, as well as data that contains a gravitational-wave event after subtracting the signal using the best-fit template. Finally, a comparison of our glitch identification results from real events in LIGO-Virgo’s third observing run against the list of events which required glitch mitigation shows that this tool will be useful in providing precise information about data quality to improve astrophysical analyses of these events.
