Year: 2024
Type: preprint
Abstract: The presence of a massive body between the Earth and a gravitational-wave source will produce the so-called gravitational lensing effect. In the case of strong lensing, it leads to the observation of multiple deformed copies of the initial wave. Machine-learning (ML) models have been proposed for identifying these copies much faster than optimal Bayesian methods, as will be needed with the detection rate of next-generation detector. Most of these ML models are based on a time-frequency representation of the data that discards the phase information. We introduce a neural network that directly uses the time series data to retain the phase, limit the pre-processing time and keep a one-dimensional input. We show that our model is more efficient than the base model used on time-frequency maps at any False Alarm Rate (FPR), up to $\sim 5$ times more for an FPR of $10^{-4}$. We also show that it is not significantly impacted by the choice of waveform model, by lensing-induced phase shifts and by reasonable errors on the merger time that induce a misalignment of the waves in the input.
Source: arXiv (Cornell University)
Authors: Offermans, Arthur, Li, Tjonnie G. F.
Institution: KU Leuven
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404573908