Python
- Revisiting the evidence for precession in GW200129 with machine learning noise mitigation —
- Quasi-physical model for removing short glitches from LIGO and Virgo data —
- A sensitive test of non-Gaussianity in gravitational-wave detector data —
- GLADE+: An extended galaxy catalogue for multimessenger searches with Advanced gravitational-wave detectors —
- Search for gravitational waves associated with Gamma-ray bursts detected by Fermi and Swift during the LIGO-Virgo run O3b —
- Search for gravitational waves associated with Gamma-ray bursts detected by Fermi and Swift during the LIGO–Virgo run O3a —
- LIGO detector characterization in the second and third observing runs —
- Quantum-enhanced Advanced LIGO detectors in the era of gravitational-wave astronomy —
- Search for gravitational-wave signals associated with Gamma-ray bursts during the second observing run of Advanced LIGO and Advanced Virgo —
- Bayesian inference analysis of unmodelled gravitational-wave transients —
- GLADE: A galaxy catalogue for multimessenger searches in the advanced gravitational-wave detector era —
- Gravitational waves and Gamma-rays from a binary neutron star merger: GW170817 and GRB 170817A —
- GLADE
Creating a publicly available galaxy catalog. - Gamma-ray bursts
Searching for gravitational-waves associated with gamma-ray bursts. - Non-linear noise subtraction
Broadband noise modeling with dense neural networks. - Sky localization
The effect of noise in localising gravitational-wave events. - Probabilistic noise estimation
Measuring the amount of non-Gaussian noise in the data. - Antiglitch
Probabilistic modeling of glitches in gravitational-wave data. - Fast glitch modeling
Modeling gravitational-wave glitches with autoencoders.