Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"

This repository contains the datasets used for training and testing during the work discussed in the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:

A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.

In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.

Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:

1. GPS start of the file from which this segment comes
2. starting GPS time of this segment
3. duration of the segment [s]
4. mass1 [solar masses]
5. mass2 [solar masses]
6. spin1z
7. spin2z
8. inclination [radians]
9. coalescence phase [radians]
10. distance [Mpc]
11. right_ascension [radians]
12. declination [radians]
13. polarization [radians]
14. SNR (signal to noise ratio)
15. shift of the signal w.r.t. the timeseries [s]
16. length of the signal [s]
17. fraction of the signal contained in the time window

Number of samples:

• 80000 for the file glitch_test.npz
• 69998 for the file glitch_train.npz
• 500000 for the file noise_test.npz
• 250000 for the file noise_train.npz
• 500000 for the file signal_test.npz
• 250000 for the file signal_train.npz

An example of few lines of python code to read each file is:

import numpy as np
f = np.load("filename.npz")
X = f['X']
Y = f['Y']
m = f['metadata']

For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. 

This research has made use of data or software obtained from the Gravitational Wave Open Science Center (gwosc.org), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.

[1] https://gwpy.github.io
[2] https://pycbc.org
[3] https://lscsoft.docs.ligo.org/lalsuite