gwsnr is a Python package that computes gravitational-wave signal-to-noise ratios and detection probabilities for large compact-binary populations using interpolation, neural networks, and GPU acceleration.
Uncovering faint lensed gravitational-wave signals and reprioritizing their follow-up analysis using galaxy lensing forecasts with detected counterparts
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abstract
Like light, gravitational waves can be gravitationally lensed by massive astrophysical objects. Strong gravitational lensing by galaxies and galaxy clusters is anticipated to become observable in the coming years. This phenomenon will manifest as multiple copies of the original wave, each exhibiting identical frequency evolution but distinct arrival times, amplitudes, and overall phases. Some of these images can be below the detection threshold and require targeted search methods, based on tailor-made template banks. These searches can be made more sensitive by using our knowledge of the typical distribution and morphology of lenses to predict the time delay, magnification, and image-type ordering of the lensed images. Here, we show that when a subset of the galaxy lensed images is super-threshold, they can be used to construct a more constrained prediction of the arrival time of the remaining signals, enhancing our ability to identify lensing candidate signals. Our suggested method effectively reduces the list of triggers requiring follow-up and generally re-ranks the genuine counterpart higher in the lensing candidate list. So, using information provided by the two or three super-threshold images, one can identify additional lensed images, also strengthening the evidence for the lensed signal hypothesis.
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gwsnr: A Python package for efficient signal-to-noise ratio calculations of gravitational waves
gwsnr is a Python package that computes gravitational-wave signal-to-noise ratios and detection probabilities for large compact-binary populations using interpolation, neural networks, and GPU acceleration.