arXiv subjects

Stefano Dal Pra

Publications and source records attributed to Stefano Dal Pra.

11 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↗

arXiv2025

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↗

arXiv2025

The AI_INFN Platform: Artificial Intelligence Development in the Cloud

Machine Learning (ML) is profoundly reshaping the way researchers create, implement, and operate data-intensive software. Its adoption, however, introduces notable challenges for computing infrastructures, particularly when it comes to coordinating access to hardware accelerators across development, testing, and production environments. The INFN initiative AI_INFN (Artificial Intelligence at INFN) seeks to promote the use of ML methods across various INFN research scenarios by offering comprehensive technical support, including access to AI-focused computational resources. Leveraging the INFN Cloud ecosystem and cloud-native technologies, the project emphasizes efficient sharing of accelerator hardware while maintaining the breadth of the Institute's research activities. This contribution describes the deployment and commissioning of a Kubernetes-based platform designed to simplify GPU-powered data analysis workflows and enable their scalable execution on heterogeneous distributed resources. By integrating offloading mechanisms through Virtual Kubelet and the InterLink API, the platform allows workflows to span multiple resource providers, from Worldwide LHC Computing Grid sites to high-performance computing centers like CINECA Leonardo. We will present preliminary benchmarks, functional tests, and case studies, demonstrating both performance and integration outcomes.

cs.DC↗

arXiv2025

A directed continuous-wave search from neutron stars in binary systems with the five-vector resampling technique

Continuous gravitational-wave signals (CWs), which are typically emitted by rapidly rotating neutron stars with non-axisymmetric deformations, represent particularly intriguing targets for the Advanced LIGO-Virgo-KAGRA detectors. These detectors operate within sensitivity bands that encompass more than half of the known pulsars in our galaxy existing in binary systems, which are the targeted sources of this paper. However, the detection of these faint signals is especially challenged by the Doppler modulation due to the source's orbital motion, typically described by five Keplerian parameters, which must be determined with high precision to effectively detect the signal. This modulation spreads the signal across multiple frequency bins, resulting in a notable reduction of signal-to-noise ratio and potentially hindering signal detection. To overcome this issue, a robust five-vector resampling data-analysis algorithm has been developed to conduct thorough directed/narrowband CW searches at an affordable computational cost. We employ this methodology for the first time to search for CWs from Scorpius X-1, using publicly available data from the third observing run of the Advanced LIGO-Virgo-KAGRA detectors. No statistically significant CW signals can be claimed. Hence, we proceeded setting 95% confidence-level upper limits in selected frequency bands and orbital parameter ranges, while also evaluating overall sensitivity.

gr-qc↗

arXiv2025

A directed continuous-wave search from Scorpius X-1 with the five-vector resampling technique

Continuous gravitational-wave signals (CWs), which are typically emitted by rapidly rotating neutron stars with non-axisymmetric deformations, represent particularly intriguing targets for the Advanced LIGO-Virgo-KAGRA detectors. These detectors operate within sensitivity bands that encompass more than half of the known pulsars in our galaxy existing in binary systems (i.e., over 417 pulsars), which are the targeted sources of this paper. However, the detection of these faint signals is especially challenged by the Doppler modulation due to the source's orbital motion, typically described by five Keplerian parameters, which must be determined with high precision to effectively detect the signal. This modulation spreads the signal across multiple frequency bins, resulting in a notable reduction of signal-to-noise ratio and potentially hindering signal detection. To overcome this issue, a robust five-vector resampling data-analysis algorithm has been developed to conduct thorough directed/narrowband CW searches at an affordable computational cost. We employ this methodology for the first time to search for CWs from Scorpius X-1, using publicly available data from the third observing run of the Advanced LIGO-Virgo-KAGRA detectors. No statistically significant CW signals can be claimed. Hence, we proceeded setting 95% confidence-level upper limits in selected frequency bands and orbital parameter ranges, while also evaluating overall sensitivity.

gr-qc↗

arXiv2025

Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform

Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence at INFN") aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as effectively as possible, ensuring the diversity of the Institute's research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.

cs.DC↗

arXiv2024

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↗

arXiv2024

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↗

arXiv2020

Extension of the INFN Tier-1 on a HPC system

The INFN Tier-1 located at CNAF in Bologna (Italy) is a center of the WLCG e-Infrastructure, supporting the 4 major LHC collaborations and more than 30 other INFN-related experiments. After multiple tests towards elastic expansion of CNAF compute power via Cloud resources (provided by Azure, Aruba and in the framework of the HNSciCloud project), and building on the experience gained with the production quality extension of the Tier-1 farm on remote owned sites, the CNAF team, in collaboration with experts from the ALICE, ATLAS, CMS, and LHCb experiments, has been working to put in production a solution of an integrated HTC+HPC system with the PRACE CINECA center, located nearby Bologna. Such extension will be implemented on the Marconi A2 partition, equipped with Intel Knights Landing (KNL) processors. A number of technical challenges were faced and solved in order to successfully run on low RAM nodes, as well as to overcome the closed environment (network, access, software distribution, ... ) that HPC systems deploy with respect to standard GRID sites. We show preliminary results from a large scale integration effort, using resources secured via the successful PRACE grant N. 2018194658, for 30 million KNL core hours.

physics.comp-ph↗

arXiv2019

INDIGO-DataCloud:A data and computing platform to facilitate seamless access to e-infrastructures

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications.

cs.DC↗

arXiv2017

Improved Cloud resource allocation: how INDIGO-DataCloud is overcoming the current limitations in Cloud schedulers

Performing efficient resource provisioning is a fundamental aspect for any resource provider. Local Resource Management Systems (LRMS) have been used in data centers for decades in order to obtain the best usage of the resources, providing their fair usage and partitioning for the users. In contrast, current cloud schedulers are normally based on the immediate allocation of resources on a first-come, first-served basis, meaning that a request will fail if there are no resources (e.g. OpenStack) or it will be trivially queued ordered by entry time (e.g. OpenNebula). Moreover, these scheduling strategies are based on a static partitioning of the resources, meaning that existing quotas cannot be exceeded, even if there are idle resources allocated to other projects. This is a consequence of the fact that cloud instances are not associated with a maximum execution time and leads to a situation where the resources are under-utilized. These facts have been identified by the INDIGO-DataCloud project as being too simplistic for accommodating scientific workloads in an efficient way, leading to an underutilization of the resources, a non desirable situation in scientific data centers. In this work, we will present the work done in the scheduling area during the first year of the INDIGO project and the foreseen evolutions.

cs.DC↗