# G2Net Gravitational Wave Detection — Data

来源：https://www.kaggle.com/competitions/g2net-gravitational-wave-detection/data

采集时间：2026-10-24T15:01:40Z

In this competition you are provided with a training set of time series data containing simulated gravitational wave measurements from a network of 3 gravitational wave interferometers (LIGO Hanford, LIGO Livingston, and Virgo). Each time series contains either detector noise or detector noise plus a simulated gravitational wave signal. The task is to identify when a signal is present in the data (`target=1`).

The parameters that determine the exact form of a binary black hole waveform are the masses, sky location, distance, black hole spins, binary orientation angle, gravitational wave polarisation, time of arrival, and phase at coalescence (merger). These parameters (15 in total) have been randomised according to astrophysically motivated prior distributions and used to generate the simulated signals present in the data, but are not provided as part of the competition data.

Each data sample (`npy` file) contains 3 time series (1 for each detector) and each spans 2 sec and is sampled at 2,048 Hz.

The integrated signal-to noise ratio (SNR) is classically the most informative measure of how detectable a signal is and a typical level of detectability is when this integrated SNR exceeds ~8. This shouldn't confused with the instantaneous SNR - the factor by which the signal rises above the noise - and in nearly all cases the (unlike the first gravitational wave detection GW150914) these signals are not visible by eye in the time series. 

## Files

*   **train/** - the training set files, one `npy` file per observation; labels are provided in a files shown below
*   **test/** - the test set files; you must predict the probability that the observation contains a gravitational wave
*   **training_labels.csv** - target values of whether the associated signal contains a gravitational wave
*   **sample_submission.csv** - a sample submission file in the correct format