arXiv subjects

Francesco Safai Tehrani

Publications and source records attributed to Francesco Safai Tehrani.

7 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↗

arXiv2025

Harnessing the potential of PyStoch: detecting continuous gravitational waves from interesting supernova remnant targets

Detecting continuous gravitational waves (CWs) is challenging due to their weak amplitude and high computational demands, especially with poorly constrained source parameters. Stochastic gravitational-wave background (SGWB) searches using cross-correlation techniques can identify unresolved astrophysical sources, including CWs, at lower computational cost, albeit with reduced sensitivity. This motivates a hybrid approach where SGWB algorithms act as a first-pass filter to identify CW candidates for follow-up with dedicated CW pipelines. We evaluated the discovery potential of the SGWB analysis tool PyStoch for detecting CWs, using simulated signals from spinning down NSs. We then applied the method to data from the third LIGO-Virgo-KAGRA observing run (O3), covering the (20-1726) Hz frequency band, and targeting four supernova remnants: Vela Jr., G347.3-0.5, Cassiopeia A, and the NS associated with the 1987A supernova remnant. If necessary, significant candidates are followed up using the 5-vector Resampling and Band-Sampled Data Frequency-Hough techniques. However, since no interesting candidates were identified in the real O3 analysis, we set 95\% confidence-level upper limits on the CW strain amplitude $h_0$. The most stringent limit was obtained for Cassiopeia A, and is $h_0 = 1.13 \times 10^{-25}$ at $201.57$ Hz with a frequency resolution of $1/32$ Hz. As for the other targets, the best upper limits have been set with the same frequency resolution, and correspond to $h_0 = 1.20 \times 10^{-25} $ at $202.16$ Hz for G347.3-0.5, $1.20 \times 10^{-25}$ at $217.81$ Hz for Vela Jr., and $1.47 \times 10^{-25}$ at $186.41$ Hz for the NS in the 1987A supernova remnant.

astro-ph.HE↗

arXiv2025

Doppler correlation-driven vetoes for the Frequency Hough analysis in continuous gravitational-wave searches

We present an improved method for vetoing candidates of continuous gravitational-wave sources during all-sky searches utilizing the Frequency Hough pipeline. This approach leverages linear correlations between source parameters induced by the Earth Doppler effect, which can be effectively identified through the Hough Transform. Candidates that do not align with these patterns are considered spurious and can thus be vetoed, enhancing the depth and statistical significance of follow-up analyses. Additionally, we provide a comprehensive explanation of the method calibration, which intrinsically linked to the total duration of the observing run. On average, the procedure successfully vetoes $56\%$ of candidates. To assess the method performance, we conducted a Monte-Carlo simulation injecting fake continuous-wave signals into data from the third observing run of the LIGO detectors. This analysis allowed us to infer strain amplitude upper limits at a $90\%$ confidence level. We found that the optimal sensitivity is $h_0^{90\%} = 3.62^{+0.23}_{-0.22}\times 10^{-26}$ in the [128, 200] Hz band, which is within the most sensible frequency band of the LIGO detectors.

gr-qc↗

arXiv2025

Neural network method to search for long transient gravitational waves

We present a new method to search for long transient gravitational waves signals, like those expected from fast spinning newborn magnetars, in interferometric detector data. Standard search techniques are computationally unfeasible (matched filtering) or very demanding (sub-optimal semi-coherent methods). We explored a different approach by means of machine learning paradigms, to define a fast and inexpensive procedure. We used convolutional neural networks to develop a classifier that is able to discriminate between the presence or the absence of a signal. To complement the classification and enhance its effectiveness, we also developed a denoiser. We studied the performance of both networks with simulated colored noise, according to the design noise curve of LIGO interferometers. We show that the combination of the two models is crucial to increase the chance of detection. Indeed, as we decreased the signal initial amplitude (from $10^{-22}$ down to $10^{-23}$) the classification task became more difficult. In particular, we could not correctly tag signals with an initial amplitude of $2 \times 10^{-23}$ without using the denoiser. By studying the performance of the combined networks, we found a good compromise between the search false alarm rate (2$\%$) and efficiency (90$\%$) for a single interferometer. In addition, we demonstrated that our method is robust with respect to changes in the power law describing the time evolution of the signal frequency. Our results highlight the computationally low cost of this method to generate triggers for long transient signals. The study carried out in this work lays the foundations for further improvements, with the purpose of developing a pipeline able to perform systematic searches of long transient signals.

astro-ph.IM↗

arXiv2021

Il progetto Lab2Go per la diffusione della pratica laboratoriale nelle Scuole Secondarie di II grado

Even if laboratory practice is essential for all scientific branches of knowledge, it is often neglected at High School, due to lack of time and/or resources. To establish a closer contact between school and experimental sciences, the University Sapienza of Roma and the Istituto Nazionale di Fisica Nucleare (INFN) launched the Lab2Go project, with the goal of spreading laboratory practice among students and teachers in high schools.

physics.ed-ph↗