# name
Detection of Gravitational Wave signals using Machine Learning methods and Generative Pre-trained Transformers

# metadata.dc.description[].value
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2023, Tutors: Tomás Andrade Weber, Roberto Emparan García de Salazar

# metadata.dc.description.abstract[].value
We use Machine Learning methods based on Convolutional Neural Networks to search for gravitational waves signals above the background noise distribution for a data set of simulated gravitational waves and real noise signals from three detectors (LIGO Hanford, LIGO Livingston, and Virgo). A training data set is used to train the ML method to classify data streams in two groups: gravitational wave plus noise (label 1) or only noise (label 0). Later, the method predicts if data streams from a testing data set belong to one or an other category. To generate the code that implements the CNN algorithm we use Generative Pre-trained Transformers, specifically ChatGPT based on GPT-3 and compare them to a human-made CNN. The ML methods are capable to detect
gravitational waves if we give ChatGPT freedom to create a CNN without specifying the parameters or the architecture, but are not satisfactory if we try to direct ChatGPT to a specific type of code.

# metadata.dc.title[].value
Detection of Gravitational Wave signals using Machine Learning methods and Generative Pre-trained Transformers