17 citations · 20 across the 5 of their papers we have counts for
5 papers
Robustness of Deep Learning Models to Precession in Gravitational-Wave Searches for Intermediate-Mass Black Hole Binaries
Quirijn Meijer, Marc van der Sluys, Sarah Caudill
Gravitational-wave searches for signals of intermediate-mass black hole binaries are hindered by detector glitches, as the increased masses from stellar-mass systems hinder current…
Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs
Ana Martins, Melissa Lopez, Quirijn Meijer +4
The detection of gravitational waves (GWs) from binary neutron stars (BNSs) with possible telescope follow-ups opens a window to ground-breaking discoveries in the field of multi-m…
Fast Waveform Generation for Gravitational Waves using Evolutionary Algorithms
Quirijn Meijer, Sarah Caudill
Gravitational-wave analyses depend heavily on waveforms that model the evolution of compact binary coalescences as seen by observing detectors. In many cases these waveforms are gi…
Relative binning for complete gravitational-wave parameter estimation with higher-order modes and precession, and applications to lensing and third-generation detectors
Harsh Narola, Justin Janquart, Quirijn Meijer +2
Once a gravitational wave signal is detected, the measurement of its source parameters is important to achieve various scientific goals. This is done through Bayesian inference, wh…
Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data using Deep Learning
Quirijn Meijer, Melissa Lopez, Daichi Tsuna +1
Gravitational-wave searches for cosmic strings are currently hindered by the presence of detector glitches, some classes of which strongly resemble cosmic string signals. This conf…