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This project aims to analyze and classify glitches in the Virgo detector gravitational wave data using Machine Learning techniques. The main goal is to study glitch morphology, explore correlations between different glitch types, and contribute to a deeper understanding of detector noise sources.

LSC Algorithm Library Suite

Documentation : Wiki

DOI: https://doi.org/10.7935/GT1W-FZ16

Quantum Bayesian waveform estimation

Project avatar taken with permission from https://pixabay.com/photos/rodrigues-fruit-bat-bat-animal-7523361/

Python oriented template repository providing support for Python project. Ready to compile including tests, examples, script and modern Doxygen documentations (GitLab Pages)

LSC Algorithm Library Suite

Documentation : Wiki

DOI: https://doi.org/10.7935/GT1W-FZ16

LSC Algorithm Library Suite

Documentation : Wiki

DOI: https://doi.org/10.7935/GT1W-FZ16

GQuEST-related work into variance estimation, and simultaneous mean and variance estimation of quantum states.

Project avatar taken with permission from https://pixabay.com/photos/pheasant-bird-animal-feather-5864490/

Finesse 3.0 models, data, and tools for the Virgo interferometer

Training Neural Networks to improve the quality of the output of the GSTlal pipeline.