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1. Compact binary coalescences: constraints on waveforms

   https://doi.org/10.1007/s10714-020-02764-1  

   Ashtekar, A.; De Lorenzo, T.; Khera, N. (October 2020, General relativity and gravitation)

   Gravitational waveforms for compact binary coalescences (CBCs) have been invaluable for detections by the LIGO-Virgo collaboration. They are obtained by a combination of semi-analytical models and numerical simulations. So far systematic errors arising from these procedures appear to be less than statistical ones. However, the significantly enhanced sensitivity of the new detectors that will become operational in the near future will require waveforms to be much more accurate. This task would be facilitated if one has a variety of cross-checks to evaluate accuracy, particularly in the regions of parameter space where numerical simulations are sparse. Currently errors are estimated by comparing the candidate waveforms with the numerical relativity (NR) ones, which are taken to be exact. The goal of this paper is to propose a qualitatively different tool. We show that full non-linear general relativity (GR) imposes an infinite number of sharp constraints on the CBC waveforms. These can provide clear-cut measures to evaluate the accuracy of candidate waveforms against exact GR, help find systematic errors, and also provide external checks on NR simulations themselves. 

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2. Ultrasound Waveforms with and without Ringdown Artifacts

   https://doi.org/10.21227/z6v5-mf23  

   Sosnovskaya, Yana Sosnovskaya (January 2022, IEEE DataPort)

   Minimally-Invasive Surgeries can benefit from having miniaturized sensors on surgical graspers to provide additional information to the surgeons. One such potential sensor is an ultrasound transducer. At long travel distances, the ultrasound transducer can accurately measure its ultrasound wave's time of flight, and from it, classify the grasped tissue. However, the ultrasound transducer has a ringing artifact arising from the decaying oscillation of its piezo element, and at short travel distances, the artifact blends with the acoustic echo. Without a method to remove the artifact from the blended signal, this makes it impossible to measure the waveform's time of flight.It is possible to use both classical signal processing and deep learning methods to filter raw ultrasound signals, removing the ringing artifact from them, and from the filtered signals, to obtain the time of flight. In this dataset, two datasets are provided to train and test algorithms developed for filtering out the ringdown artifact, and for subsequently extracting the waveform's time of flight. All measured (raw) signals were collected the same experimental setup: an oscilloscope connected to an ultrasound driver to drive a transducer attached to a liquid water container, in an attempt to mimic tissue properties in a tightly controlled environment.The training dataset consists of two groups of signal pairs. The first group consists of 993 signal pairs, with each pair consisting of a raw ultrasound signal (with the acoustic echo blended with the ringing artifact), and a target filtered signal (with only the desired echo). Signals in the first group are sampled at the original sampling frequency of 500 MHz. The second group is like the first group, but with all signals downsampled by a factor of 26. This training dataset includes only travel distances from 2 cm to 4 cm, inclusively, because at these distances in water, the echo is sufficiently separated from the ringdown artifact to be manually extractable. The signal pairs are approximately equally distributed between the distances covered.The test dataset similarly consists of two groups of raw ultrasound signals. The first group consists of 270 signals, collected at 9 travel distances between 0.5 cm and 4.0 cm, with 30 signals per distance. It also includes the associated true times of flight for each distance. Signals in the first group are sampled at the original sampling frequency of 500 MHz. The second group is like the first group, but with all signals downsampled by a factor of 26. All signals in both datasets are aligned. 

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3. Astrophysical Tau Neutrino Identification with IceCube Waveforms

   https://doi.org/10.22323/1.358.1036  

   Wille, Logan; Xu, Donglian (January 2020, Pos proceedings of science)
4. Analytic waveforms for eccentric gravitational wave bursts

   https://doi.org/10.1088/1361-6382/ab745f  

   Loutrel, Nicholas (April 2020, Classical and Quantum Gravity)
5. Modeling compact binary merger waveforms beyond general relativity

   https://doi.org/10.1103/PhysRevD.107.024015  

   Bonilla, Gabriel S.; Kumar, Prayush; Teukolsky, Saul A. (January 2023, Physical Review D)