Alessandro Martini | Searcharxiv

arXiv2025

Optimizing searches for gravitational wave bursts using coherent WaveBurst 2G

The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal waveform, direction, or arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available emission models and for discovering yet-to-be-uncovered sources. Here, we discuss progress in the detection performance of the coherent WaveBurst second-generation pipeline (cWB-2G), which is highly adaptable to both minimally modeled and model-informed searches for GWTs. Several search configurations for GWTs are examined using approximately 14.8 days of observation time from the third observing run by LIGO-Virgo-KAGRA (LVK). Recent enhancements include a ranking statistic fully based on multivariate classification with eXtreme Gradient Boosting, a thorough validation of the statistical significance accuracy of GWT candidates, and a measurement of the correlations of false alarms and simulated detections between different concurrent searches. For the first time, we provide a comprehensive comparison of cWB-2G performance on data from networks made of two and three detectors, and we demonstrate the advantage of combining concurrent searches for GWTs of generic morphology in a global observatory. This work offers essential insights for assessing our data analysis strategies in ongoing and future LVK searches for generic GWTs.

gr-qc↗

arXiv2021

Maximum Entropy Spectral Analysis: an application to gravitational waves data analysis

The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg, offers a powerful tool for spectral estimation of a time-series. It relies on Jaynes' maximum entropy principle, allowing the spectrum of a stochastic process to be inferred using the coefficients of an autoregressive process AR($p$) of order $p$. A closed-form recursive solution provides estimates for both the autoregressive coefficients and the order $p$ of the process. We provide a ready-to-use implementation of this algorithm in a Python package called \texttt{memspectrum}, characterized through power spectral density (PSD) analysis on synthetic data with known PSD and comparisons of different criteria for stopping the recursion. Additionally, we compare the performance of our implementation with the ubiquitous Welch algorithm, using synthetic data generated from the GW150914 strain spectrum released by the LIGO-Virgo-Kagra collaboration. Our findings indicate that Burg's method provides PSD estimates with systematically lower variance and bias. This is particularly manifest in the case of a small (O($5000$)) number of data points, making Burg's method most suitable to work in this regime. Since this is close to the typical length of analysed gravitational waves data, improving the estimate of the PSD in this regime leads to more reliable posterior profiles for the system under study. We conclude our investigation by utilising MESA, and its particularly easy parametrisation where the only free parameter is the order $p$ of the AR process, to marginalise over the interferometers noise PSD in conjunction with inferring the parameters of GW150914.

stat.ME↗