arXiv subjects

Massimo Lenti

Publications and source records attributed to Massimo Lenti.

3 recordsLinked to original sources

arXiv2026

Reducing the Virgo site infrastructure noise in preparation of the O4 observing run

The heating, ventilation and air conditioning systems serving the experimental halls of the Virgo gravitational wave interferometer generate low-frequency noise - namely below 100 Hz - of seismic, acoustic, and electromagnetic origin. Such disturbances have repeatedly affected the interferometer sensitivity throughout its operational history, with particularly notable impacts during the third observing run. In preparation for the fourth run, a comprehensive investigation was carried out to identify the most critical noise sources within this infrastructure and to trace their transmission paths into the experimental areas. This manuscript presents the methodology and results of the noise characterization campaign, together with the design, implementation and assessment of targeted mitigation measures. The technical solutions adopted, along with the operational best practices developed, provide valuable guidance for the design of low-noise environments in future gravitational-wave observatories.

astro-ph.IM↗

arXiv2026

The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference

Sequential Monte Carlo (SMC) methods have recently been applied to gravitational-wave inference as a powerful alternative to standard sampling techniques, such as Nested Sampling. At the same time, gradient-based Markov Chain Monte Carlo algorithms, most notably the No-U-Turn Sampler (NUTS), provide an efficient way to explore high-dimensional parameter spaces. In this work we present SHARPy, a Bayesian inference framework that combines the parallelism and evidence-estimation capabilities of SMC with the state-of-the-art sampling performance of NUTS. Moreover, SHARPy exploits the local geometric structure of the posterior to further improve efficiency. Built on JAX, a high-performance computing framework that enables automatic differentiation and hardware acceleration, SHARPy performs gravitational-wave inference on binary black-hole events in around ten minutes, yielding posterior samples and Bayesian evidence estimates that are consistent with those obtained through Nested Sampling. This work sets a new milestone in Gravitational-Wave inference with likelihood-based methods and paves the way for model comparison tasks to be accomplished in minutes.

gr-qc↗

arXiv2024

Enhancing gravitational-wave host localization with SKYFAST: rapid volume and inclination angle reconstruction

The scientific impact of GW170817 strongly supports the need for an efficient electromagnetic follow-up campaign to gravitational-wave event candidates. The success of such campaigns depends critically on a fast and accurate localization of the source. In this paper, we present SKYFAST, a new pipeline for rapid localization of gravitational-wave event hosts. SKYFAST runs alongside a full parameter estimation (PE) algorithm, from which posterior samples are taken. It uses these samples to reconstruct an analytical posterior for the sky position, luminosity distance, and inclination angle using a Dirichlet Process Gaussian Mixture Model, a Bayesian non-parametric method. This approach allows us to provide an accurate localization of the event using only a fraction of the total samples produced by the full PE analysis. Depending on the PE algorithm employed, this can lead to significant time savings, which is crucial for identifying the electromagnetic counterpart. Additionally, in a few minutes, SKYFAST generates a ranked list of the most probable galaxy hosts from a galaxy catalog of choice. This list includes information on the inclination angle posterior conditioned to the position of each candidate host, which is useful for assessing the detectability of gamma-ray burst structured jet emissions.

astro-ph.HE↗