arXiv subjects

Lorenzo Silvestri

Publications and source records attributed to Lorenzo Silvestri.

3 recordsLinked to original sources

arXiv2026

Search for Long-Transient Gravitational Waves from Supernova SN2023ixf using GFH-v2 Pipeline

We present a directed search for long-transient gravitational waves from the possible newborn magnetar remnant of SN2023ixf, a nearby Type II core-collapse supernova in the M101 galaxy. The analysis uses LIGO Hanford and Livingston data from Engineering Run 15, using coincident data lying within the on-source window associated with the supernova. We target signals from a rapidly rotating, non-axisymmetric neutron star whose spin-down is dominated by gravitational-wave emission, producing a power-law decrease in frequency and a corresponding decrease in strain amplitude. The search is performed with the GFH-v2 pipeline, based on the Generalized Frequency Hough transform. No candidate survives the coincidence and follow-up analysis. We therefore set upper limits on the maximum detectable distance as a function of initial frequency and ellipticity. For the highest ellipticity interval, the 90% upper limits reach distances of about 1-2.5~Mpc across most of the analysed band. Although these limits are below the distance to M101, the search provides the first application of GFH-v2 to a nearby core-collapse supernova and characterizes its performance on real detector data.

astro-ph.IM↗

arXiv2026

GFH-v2 Pipeline for Searches of Long-Transient Gravitational Waves from Newborn Magnetars

This paper presents an enhanced methodology for searching long transient gravitational waves associated with a newborn magnetar, with particular focus on the regime in which the early spin-down is dominated by gravitational-wave emission. The analysis is performed using a strongly improved version of the generalized Frequency Hough Transform algorithm, called GFH-v2. We describe the main developments introduced relative to the original implementation and outline the optimized parameter-space selection used in the search. We then compute the theoretical sensitivity of the method and compare it with an empirical sensitivity estimate obtained by injecting simulated signals into LIGO-Virgo-KAGRA O4a data. The updated framework achieves improved sensitivity and computational performance. These results provide a robust basis for future directed searches for long-transient gravitational-wave signals from core-collapse supernovae and other transient events in current and upcoming observing runs.

astro-ph.IM↗

arXiv2026

py5vec: a modular Python package for the 5-vector method to search for continuous gravitational waves

We present \texttt{py5vec}, a Python package for implementing and extending the 5-vector method, used to search for continuous gravitational wave (CW) signals. We also provide a comprehensive theoretical review of the 5-vector method and extend the relative likelihood formalism by marginalizing over the noise variance, resulting in a more robust Student's t-likelihood, and over the initial phase to account for pulsar glitches. \texttt{py5vec} provides a modular architecture that separates data representation, signal demodulation, and statistical inference into independent abstract stages. It supports multiple input data formats and interoperates with existing Python software, providing a bridge between different approaches. For example, using a \texttt{bilby}-based interface, \texttt{py5vec} implements Bayesian parameter estimation within the 5-vector formalism for the first time. The modular design also allows for making exact multi-level and direct comparisons between other software, such as \texttt{cwinpy} and \texttt{SNAG} in MATLAB. In \texttt{py5vec}, we implement a multidetector targeted search for known pulsars, validated using LIGO data from the O4a run and hardware injections, demonstrating consistent reconstruction of signal parameters. This package therefore provides a flexible platform for current targeted searches and for future extensions to other CW search strategies.

astro-ph.IM↗