arXiv subjects

Carlo Giunchi

Publications and source records attributed to Carlo Giunchi.

8 recordsLinked to original sources

arXiv2026

The impact of seasonality over the sensitivity of Einstein Telescope and the SNR of CBC signals at the Sardinia candidate site

This work presents the first investigation of the impact of seasonal variations in seismic noise on the low-frequency performance of the Einstein Telescope (ET) at the Sardinia candidate site, focusing on their effect on compact binary coalescence signals. Using seismic data collected between 2022 and 2025 from a deep borehole, we characterize the seasonal variability of the seismic noise and identify the months of July and December as representative best- and worst-case conditions. The measured seismic spectra are used to estimate the Newtonian noise contribution in the 2--10 Hz frequency range and to derive corresponding modifications to the ET design sensitivity. These sensitivity curves are then used in simulations of binary neutron star and intermediate-mass black hole signals in the triangular ET configuration to quantify the resulting changes in signal-to-noise ratio. We find that median seasonal conditions produce only minor changes in the expected SNR, with deviations from the design case typically below 2$\%$. Larger effects are found for the upper tail of the December noise distribution, where the average SNR loss reaches approximately 20--25$\%$. These results indicate that seasonal seismic variability at the Sardinia candidate site has a limited impact on the low-frequency performance of ET under typical conditions.

gr-qc↗

arXiv2025

The impact of local noise recorded at the ET candidate sites on the signal to noise ratio of CBC gravitational wave signals for the ET triangle configuration

We present an evaluation of how site dependent noise can affect the signal to noise ratio (SNR) of compact binary coalescence (CBC) signals in the future 3rd generation gravitational wave (GW) detector Einstein Telescope (ET). The design of ET is currently pushing the scientific community to study its scientific potential with respect to known, and possibly unexpected, GW signals using its design sensitivity curves. However, local ambient noise may have an impact on the ET sensitivity at low frequency and therefore affect the SNR of CBC signals at low frequency. Therefore, we study the impact of ambient noise on the ET sensitivity curve at the two sites candidate to host ET - Sardinia, in Italy, and the Euregio Meuse-Rhine (EMR) at the Netherlands-Belgium border - and infer the impact on the ET sensitivity curve and how the SNR of CBC signals at low frequencies is affected. We find that Sardinia shows results which are on par, if not better, than the design case. On the other hand, ambient noise for the current EMR sensitivity curve in Terziet causes a higher degradation of the SNR performances.

gr-qc↗

arXiv2024

Correlated 0.01Hz-40Hz seismic and Newtonian noise and its impact on future gravitational-wave detectors

We report correlations in underground seismic measurements with horizontal separations of several hundreds of meters to a few kilometers in the frequency range 0.01Hz to 40Hz. These seismic correlations could threaten science goals of planned interferometric gravitational-wave detectors such as the Einstein Telescope as well as atom interferometers such as MIGA and ELGAR. We use seismic measurements from four different sites, i.e. the former Homestake mine (USA) as well as two candidate sites for the Einstein Telescope, Sos Enattos (IT) and Euregio Maas-Rhein (NL-BE-DE) and the site housing the MIGA detector, LSBB (FR). At all sites, we observe significant coherence for at least 50% of the time in the majority of the frequency region of interest. Based on the observed correlations in the seismic fields, we predict levels of correlated Newtonian noise from body waves. We project the effect of correlated Newtonian noise from body waves on the capabilities of the triangular design of the Einstein Telescope's to observe an isotropic gravitational-wave background (GWB) and find that, even in case of the most quiet site, its sensitivity will be affected up to $\sim$20Hz. The resolvable amplitude of a GWB signal with a negatively sloped power-law behaviour would be reduced by several orders of magnitude. However, the resolvability of a power-law signal with a slope of e.g. $\alpha=0$ ($\alpha=2/3$) would be more moderately affected by a factor $\sim$ 6-9 ($\sim$3-4) in case of a low noise environment. Furthermore, we bolster confidence in our results by showing that transient noise features have a limited impact on the presented results.

gr-qc↗

arXiv2022

External environmental noise influences on Virgo during O3

Sources of geophysical noise, such as wind, sea waves and earthquakes, can have an impact on gravitational wave interferometers causing sensitivity worsening and gaps in data taking. During the 1-year long O3 run (April 1st 2019 to March 27th 2020), the Virgo Collaboration collected a statistically significant dataset to study the response of the detector to a variety of environmental conditions. We used these data to correlate environmental parameters to global detector performance, such as observation range, duty cycle and control losses. Where possible, we identified weaknesses in the detector and we elaborated strategies to improve Virgo robustness against external disturbances for the next run O4, planned to start in summer 2022. In this article we present preliminary results of this study.

gr-qc↗

arXiv2021

SeisBench -- A Toolbox for Machine Learning in Seismology

Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large datasets often provides significant improvements over traditional techniques. With the entire spectrum of seismological tasks, e.g., seismic picking, source property estimation, ground motion prediction, hypocentre determination; among others, now incorporating ML approaches, numerous models are emerging as these techniques are further adopted within seismology. To evaluate these algorithms, quality controlled benchmark datasets that contain representative class distributions are vital. In addition to this, models require implementation through a common framework to facilitate comparison. Accessing these various benchmark datasets for training and implementing the standardization of models is currently a time-consuming process, hindering further advancement of ML techniques within seismology. These development bottlenecks also affect "practitioners" seeking to deploy the latest models on seismic data, without having to necessarily learn entirely new ML frameworks to perform this task. We present SeisBench as a software package to tackle these issues. SeisBench is an open-source framework for deploying ML in seismology. SeisBench standardises access to both models and datasets, whilst also providing a range of common processing and data augmentation operations through the API. Through SeisBench, users can access several seismological ML models and benchmark datasets available in the literature via a single interface. SeisBench is built to be extensible, with community involvement encouraged to expand the package. Having such frameworks available for accessing leading ML models forms an essential tool for seismologists seeking to iterate and apply the next generation of ML techniques to seismic data.

physics.geo-ph↗

arXiv2021

Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers

Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches and even achieve human-like performance under certain circumstances. However, as most studies differ in the datasets and exact evaluation tasks studied, it is yet unclear how the different approaches compare to each other. Furthermore, there are no systematic studies how the models perform in a cross-domain scenario, i.e., when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark study. We compare six previously published deep learning models on eight datasets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with EQTransformer having a small advantage for teleseismic data. Furthermore, we conduct a cross-domain study, in which we analyze model performance on datasets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but not from regional to teleseismic data or vice versa. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily compare new models or evaluate performance on new datasets, beyond those presented here. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models in seismological analysis.

physics.geo-ph↗

arXiv2020

Lunar Gravitational-Wave Antenna

Monitoring of vibrational eigenmodes of an elastic body excited by gravitational waves was one of the first concepts proposed for the detection of gravitational waves. At laboratory scale, these experiments became known as resonant-bar detectors first developed by Joseph Weber in the 1960s. Due to the dimensions of these bars, the targeted signal frequencies were in the kHz range. Weber also pointed out that monitoring of vibrations of Earth or Moon could reveal gravitational waves in the mHz band. His Lunar Surface Gravimeter experiment deployed on the Moon by the Apollo 17 crew had a technical failure rendering the data useless. In this article, we revisit the idea and propose a Lunar Gravitational-Wave Antenna (LGWA). We find that LGWA could become an important partner observatory for joint observations with the space-borne, laser-interferometric detector LISA, and at the same time contribute an independent science case due to LGWA's unique features. Technical challenges need to be overcome for the deployment of the experiment, and development of inertial vibration sensor technology lays out a future path for this exciting detector concept.

gr-qc↗

arXiv2020

Site-selection criteria for the Einstein Telescope

The Einstein Telescope (ET) is a proposed next-generation, underground gravitational-wave (GW) detector to be based in Europe. It will provide about an order of magnitude sensitivity increase with respect to currently operating detectors, and furthermore, extend the observation band towards lower frequencies, i.e., down to about 3Hz. One of the first decisions that needs to be made is about the future ET site following an in-depth site characterization. Site evaluation and selection is a complicated process, which takes into account science, financial, political, and socio-economic criteria. In this paper, we provide an overview of the site-selection criteria for ET, provide a formalism to evaluate the direct impact of environmental noise on ET sensitivity, and outline the necessary elements of a site-characterization campaign.

physics.ins-det↗