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Marek J. Szczepanczyk

Publications and source records attributed to Marek J. Szczepanczyk.

2 recordsLinked to original sources

Detecting Gravitational Wave Memory in the Next Galactic Core-Collapse Supernova

We present an approach to detecting (linear) gravitational wave memory in a Galactic core-collapse supernova using current interferometers. Gravitational wave memory is an important prediction of general relativity that has yet to be confirmed. Our approach uses a combination of Linear Prediction Filtering and Matched-Filtering. We present the results of our approach on data from core-collapse supernova simulations that span a range of progenitor mass and metallicity. We are able to detect gravitational wave memory out to 10 kpc. We also present the False Alarm Probabilities assuming an On-Source Window compatible with the presence of a neutrino detection. Errata: The neutrino-induced gravitational waveforms, a component of the total waveforms used to demonstrate the efficacy of the proposed detection method for gravitational wave memory, were computed incorrectly. They were underestimated by a factor of $4π$. As a result, the detection results improve and, most important, our primary conclusions are reinforced. In particular, with the corrected waveforms, see Fig. 1 and Fig. 3, the signal for D9.6-3D is in fact detectable at 1 kiloparsec, see Fig. 6 and Fig. 4. The corrected fit parameters are presented in Table 1. The false alarm probability for all three signals is impacted, as shown in Fig. 5. Fig. 5 demonstrates that our proposed method can reliably detect the D9.6-3D signal at 1 kpc for all cutoff values, but our conclusions regarding the inability to detect this signal at 10 and 100 kpc remain the same. We see a boost in the detectability of the D15-3D and D25-3D signals. We now obtain a lower false alarm probability at 10 and 100 kpc. The authors would like to acknowledge Lella et al. [87] for pointing out the discrepancy between our computed neutrino-induced waveforms and theirs, which prompted us to investigate the discrepancy and which led to the discovery of our error.

astro-ph.HE↗

Machine-learning non-stationary noise out of gravitational wave detectors

Signal extraction out of background noise is a common challenge in high precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal to noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is non-stationary, linear techniques often fail or are sub-optimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove non-stationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational wave observatory, where we could obtain an improvement of the detector gravitational wave reach without introducing any bias on the source parameter estimation.

gr-qc↗