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A Python module for performing Bayesian inference for continuous gravitational-wave signals from pulsars.

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.

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.

LVK 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/

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

Testing skewnormal models for the BBH chi_eff distribution

LVK Algorithm Library Suite

Documentation : Wiki

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

LVK Algorithm Library Suite

Documentation : Wiki

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

LVK Algorithm Library Suite

Documentation : Wiki

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