Virgo and Gravitational-Wave Computing in Europe

Bagnasco, S.

doi:10.1051/epjconf/202024507050

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The International Gravitational-Wave observatory Network (IGWN [31,32]) handles the computing and storage tasks for the worldwide collaboration on gravitational waves observation composed of LIGO [33], Virgo [34], Kagra [35] and future observatories. By design, the observatories are several thousands kilometers apart in order to improve the GW origin pinpointing capabilities, and data from multiple observatories must be used together to enhance space resolution.…”

Section: International Gravitational-wave Observatory Networkmentioning

confidence: 99%

Experience with Rucio in the wider HEP community

et al. 2021

View full text Add to dashboard Cite

Managing the data of scientific projects is an increasingly complicated challenge, which was historically met by developing experiment-specific solutions. However, the ever-growing data rates and requirements of even small experiments make this approach very difficult, if not prohibitive. In recent years, the scientific data management system Rucio has evolved into a successful open-source project that is now being used by many scientific communities and organisations. Rucio is incorporating the contributions and expertise of many scientific projects and is offering common features useful to a diverse research community. This article describes the recent experiences in operating Rucio, as well as contributions to the project, by ATLAS, Belle II, CMS, ESCAPE, IGWN, LDMX, Folding@Home, and the UK’s Science and Technology Facilities Council (STFC).

show abstract

Section: International Gravitational-wave Observatory Networkmentioning

confidence: 99%

Experience with Rucio in the wider HEP community

et al. 2021

View full text Add to dashboard Cite

show abstract

The Ligo-Virgo-KAGRA Computing Infrastructure for Gravitational-wave Research

Bagnasco

2024

EPJ Web of Conf.

Self Cite

View full text Add to dashboard Cite

The LIGO, VIRGO and KAGRA Gravitational-wave (GW) observatories are getting ready for their fourth observational period, O4, scheduled to begin in March 2023, with improved sensitivities and thus higher event rates. GW-related computing has both large commonalities with HEP computing, particularly in the domain of offline data processing and analysis, and important differences, for example in the fact that the amount of raw data doesn’t grow much with the instrument sensitivity, or the need to timely generate and distribute “event candidate alerts” to EM and neutrino observatories, thus making gravitational multi-messenger astronomy possible. Data from the interferometers are exchanged between collaborations both for low-latency and offline processing; in recent years, the three collaborations designed and built a common distributed computing infrastructure to prepare for a growing computing demand, and to reduce the maintenance burden of legacy custom-made tools, by increasingly adopting tools and architectures originally developed in the context of HEP computing. So, for example, HTCondor is used for workflow management, Rucio for many data management needs, CVMFS for code and data distribution, and more. We will present GW computing use cases and report about the architecture of the computing infrastructure as will be used during O4, as well as some planned upgrades for the subsequent observing run O5.

show abstract

WaveFormer: transformer-based denoising method for gravitational-wave data

Wang,

Zhou,

Cao

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and overwhelming noise in gravitational wave (GW) observational data. Though recent machine learning-based studies have shown promising results for data denoising, they are unable to precisely recover both the GW signal amplitude and phase. To address such an issue, we develop a deep neural network centered workflow, WaveFormer, for significant noise suppression and signal recovery on observational data from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The WaveFormer has a science-driven architecture design with hierarchical feature extraction across a broad frequency spectrum. As a result, the overall noise and glitch are decreased by more than one order of magnitude and the signal recovery error is roughly 1% and 7% for the phase and amplitude, respectively. Moreover, on 75 reported binary black hole (BBH) events of LIGO we obtain a significant improvement in inverse false alarm rate. Our work highlights the potential of large neural networks in gravitational wave data analysis and, while primarily demonstrated on LIGO data, its adaptable design indicates promise for broader application within the International Gravitational-Wave Observatories Network (IGWN) in future observational runs.

show abstract

Virgo and Gravitational-Wave Computing in Europe

Cited by 3 publications

References 21 publications

Experience with Rucio in the wider HEP community

Experience with Rucio in the wider HEP community

The Ligo-Virgo-KAGRA Computing Infrastructure for Gravitational-wave Research

WaveFormer: transformer-based denoising method for gravitational-wave data

Contact Info

Product

Resources

About