It's been said that teamwork makes the dream work. This couldn't be truer for the breakthrough discovery of gravitational waves (GW), signals from colliding binary black holes in 2015. It required the collaboration of experts in physics, mathematics, information science, and computing. GW signals have led researchers to observe a new population of massive, stellar-origin black holes, to unlock the mysteries of neutron star mergers, and to measure the expansion of the Universe. These signals are unimaginably tiny ripples in the fabric of space-time and even though the global network of GW detectors are some of the most sensitive instruments on the planet, the signals are buried in detector noise. Analysis of GW data and the detection of these signals is a crucial mission for the growing global network of increasingly sensitive GW detectors. These challenges in data analysis and noise characterization could be solved with the help of data science.

As with the multi-disciplined approach to the discovery of GWs, additional expertise will be needed to further GW research. In particular, social and natural sciences have taken an interest in machine learning, deep learning, classification problems, data mining, and visualization to develop new techniques and algorithms to efficiently handle complex and massive data sets. The increase in computing power and the development of innovative techniques for the rapid analysis of data will be vital to the exciting new field of GW Astronomy. Potential outcomes may include increased sensitivity to GW signals, application to control and feedback systems for next-generation detectors, noise removal, data conditioning tools, and signal characterization.

G2Net is a network of Gravitational Wave, Geophysics and Machine Learning. Via an Action from COST (European Cooperation in Science and Technology), a funding agency for research and innovation networks, G2Net aims to create a broad network of scientists. From four different areas of expertise, namely GW physics, Geophysics, Computing Science and Robotics, these scientists have agreed on a common goal of tackling challenges in data analysis and noise characterization for GW detectors.

In this competition, you’ll aim to detect GW signals from the mergers of binary black holes. Specifically, you'll build a model to analyze simulated GW time-series data from a network of Earth-based detectors. 




![](https://storage.googleapis.com/kaggle-media/competitions/G2Net-gravitational-waves/800px-LIGO_measurement_of_gravitational_waves.svg.png)
*The series of images above were taken from the 2015 [paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.061102) announcing the discovery of gravitational waves from a pair of merging black holes.*

If successful, you'll play a part in solving a crucial mission in the exciting new field of GW science. With the development of new algorithms, scientists will have a better handle on the potential power of the data science community and their innovative approaches to data analysis. Moreover, it will enable closer interaction between computer science and physics, which could benefit both disciplines. Your participation can further this collaboration and the help advance this breakthrough discovery.

### Acknowledgments

We acknowledge support from the LIGO-Virgo-Kagra Collaboration of which the hosts are members. Specifically we acknowledge the use of the software resource [lalsuite](https://git.ligo.org/lscsoft/lalsuite).