arXiv subjects

Lorenzo Mobilia

Publications and source records attributed to Lorenzo Mobilia.

4 recordsLinked to original sources

arXiv2026

Enhancing online estimation of CBC parameters with the low-latency MBTA analysis

In this paper, we describe the procedure implemented in the Multi-Band Template Analysis (MBTA) search pipeline to produce online posterior distributions of compact binary coalescence (CBC) gravitational-wave parameters. This procedure relies on an SNR optimizer technique, which consists of filtering dense local template banks. We present how these banks are constructed using information from the initial detection and detail how the results of the filtering are used to estimate source parameters and provide posterior distributions. We demonstrate the performance of our procedure on simulations and compare our source parameter estimates with the results from the first part of the fourth observing run (O4a) recently released by the LIGO-Virgo-KAGRA (LVK) collaboration.

gr-qc↗

arXiv2026

Machine Learning to assess astrophysical origin of gravitational waves triggers

In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess the consistency of the signal with an inspiral template. In addition, a combination of these and other statistical properties are used to build a 're-weighted SNR' statistics. We evaluate a Random Forest classifiers on a set of double-coincidence events identified using the MBTA pipeline. The new classifier achieves a modest but consistent increase in event detection at low false positive rates relative to the standard search. Using the output statistics from the Random Forest classifier, we compute the probability of astrophysical origin for each event, denoted as $p_\mathrm{astro}$. This is then evaluated for the events listed in existing catalogs, with results consistent with those from the standard search. Finally, we search for new possible candidates using this new statistics, with $p_\mathrm{astro} > 0.5$, obtaining a new subthreshold candidate (IFAR =0.05) event at $gps: 1240423628$ .

gr-qc↗

arXiv2026

Deep Learning Search for Gravitational Waves from Compact Binary Coalescence

Gravitational wave searches rely on a combination of methods, including matched filtering, coherent analyses, and more recent machine learning based pipelines. For compact binary coalescences, where signals originate from the relativistic dynamics of compact objects, matched filtering remains a central element, but its computational cost will increase substantially with the data volumes and parameter-space coverage required by next-generation interferometers such as the Einstein Telescope. Developing complementary strategies that reduce computational load while preserving detection performance is therefore essential. We investigate a hybrid approach that combines matched-filtering concepts with Convolutional Neural Networks, enabling efficient signal searches without relying on the usual $χ^2$ rejection test. Using simulated data sets that include injected signals in Gaussian noise, transient noise, and physical effects not represented in template bank, such as eccentricity, precession and higher-order modes, we show that the method achieves a detection efficiency comparable to a standard matched-filtering search while offering a more resource efficient pipeline. These results indicate that deep learning assisted searches can support sustainable gravitational-wave data analysis in future detector eras.

gr-qc↗

arXiv2025

The MBTA Pipeline for Detecting Compact Binary Coalescences in the Fourth LIGO-Virgo-KAGRA Observing Run

In this paper, we describe the Multi-Band Template Analysis (MBTA) search pipeline dedicated to the detection of compact binary coalescence (CBC) gravitational wave signals from the data obtained by the LIGO-Virgo-KAGRA collaboration (LVK) during the fourth observing run (O4), which started in May 2023. We give details on the configuration of the pipeline and its evolution compared to the third observing run (O3). We focus here on the configuration used for the offline results of the first part of the run (O4a), which are part of the GWTC-4 catalog (in preparation). We also give a brief summary of the online configuration and highlight some of the changes implemented or considered for the second part of O4 (O4b).

gr-qc↗