arXiv subjects

Gianluca Maria Guidi

Publications and source records attributed to Gianluca Maria Guidi.

4 recordsLinked to original sources

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.

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arXiv2021

Assessing the compact-binary merger candidates reported by the MBTA pipeline in the LIGO-Virgo O3 run: probability of astrophysical origin, classification, and associated uncertainties

We describe the method used by the Multi-Band Template Analysis (MBTA) pipeline to compute the probability of astrophysical origin, $p_{astro}$, of compact binary coalescence candidates in LIGO-Virgo data from the third observing run (O3). The calculation is performed as part of the offline analysis and is used to characterize candidate events, along with their source classification. The technical details and the implementation are described, as well as the results from the first half of the third observing run (O3a) published in GWTC-2.1. The performance of the method is assessed on injections of simulated gravitational-wave signals in O3a data using a parameterization of $p_{astro}$ as a function of the MBTA combined ranking statistic. Possible sources of statistical and systematic uncertainties are discussed, and their effect on $p_{astro}$ quantified.

gr-qc↗

arXiv2021

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

We describe the Multi-Band Template Analysis (MBTA) search for gravitational waves signals from coalescences of compact objects in the LIGO-Virgo data, at the time of the third observing run (2019-2020), both for low-latency detections and for offline analysis. Details are given on the architecture and functioning of the pipeline, including transient noise mitigation strategies, parameter space for the searched signals, detection of candidates and evaluation of a false alarm rate associated to them. The performance of the low-latency search is demonstrated based on the LIGO-Virgo third observing run, during which MBTA has contributed to 42 alerts, submitting candidates with a median latency of 36 seconds. The performance of the offline search is illustrated on a subset of data collected during the second LIGO-Virgo observation run in 2017, and are quantified based on injections of simulated signal events on the same data.

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