GWDALI focuses on parameter estimations of gravitational waves generated by compact object coalescence (CBC). This software employs both Gaussian (Fisher Matrix) and Beyond-Gaussian methods to approximate the likelihood of gravitational wave events. GWDALI also addresses the challenges posed by Fisher Matrices with zero determinants. Additionally, the Beyond-Gaussian approach incorporates the Derivative Approximation for Likelihoods (DALI) algorithm, enabling a more reliable estimation process.

WALDO (Waveform AnomaLy DetectOr) flags possible anomalous Gravitational Waves from Numerical Relativity catalogs using deep learning. It uses a U-Net architecture to learn the waveform features of a dataset. After computing the mismatch between those waveforms and the neural predictions, WALDO isolates high mismatch evaluations for anomaly search.