4 papers
Sample-efficient non-Gaussian noise reduction in gravitational wave data via learnable wavelets
Arush Pimpalkar, Digvijay Wadekar, Mark Ho-Yeuk Cheung +1
We introduce , a wavelet-based neural network architecture to identify and reduce non-Gaussian noise in gravitational wave data. Traditionally, convolutional n…
Accuracy of ringdown models calibrated to numerical relativity simulations
Francesco Crescimbeni, Gregorio Carullo, Emanuele Berti +3
The ''ringdown'' stage of gravitational-wave signals from binary black hole mergers, mainly consisting of a superposition of quasinormal modes emitted by the merger remnant, is a k…
Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation
Digvijay Wadekar, Arush Pimpalkar, Mark Ho-Yeuk Cheung +9
We introduce a machine learning (ML) framework called for improving the sensitivity of gravitational wave search pipelines. Typically, search pipelines only use a s…
Searching for intermediate mass ratio binary black hole mergers in the third observing run of LIGO-Virgo-KAGRA
Mark Ho-Yeuk Cheung, Digvijay Wadekar, Ajit Kumar Mehta +6
Intermediate mass ratio inspirals (IMRIs) of binary black holes with mass ratios are astrophysically interesting sources of gravitational waves. Me…