arXiv subjects

C P L Berry

Publications and source records attributed to C P L Berry.

5 recordsLinked to original sources

arXiv2026

The Early Career Workshop of GR-Amaldi 2025

Gravitational physics and astronomy have developed rapidly over the last decade, driven by new observations and theoretical breakthroughs. As new as the science and technology of this field are, its greatest asset may be the body of early-career researchers actively engaged in driving it forward. With the aim of bringing together this community of enthusiastic scientists from a broad array of disciplines, the organisers of the GR-Amaldi meeting joined with the Gravitational-Wave Early Career Scientists organisation to create a three-day event-the Early Career Workshop. The Workshop aimed to provide a broad overview of the field's diverse scientific possibilities and introduce key theoretical foundations underpinning its science. To complement developing technical skills, the Workshop also sought to provide participants with transferable skills to aid them in their future careers. The Workshop emphasized networking and community building, offering participants opportunities to engage with peers and mentors. It encouraged interdisciplinary exchanges and cross-institutional collaboration, fostering connections across different research efforts. Collectively, these initiatives aimed to equip participants with a comprehensive understanding of the field's research and to build a more cohesive, collaborative community of early-career researchers. We summarise key points and conclusion from the various activities carried out as part of the Workshop.

physics.soc-ph↗

arXiv2026

Hunting for new glitches in LIGO data using community science

Data from ground-based gravitational-wave detectors like LIGO contain many types of noise. Glitches are short bursts of non-Gaussian noise that may hinder our ability to identify or analyse gravitational-wave signals. They may have instrumental or environmental origins, and new types of glitches may appear following detector changes. The Gravity Spy project studies glitches and their origins, combining insights from volunteers on the community-science Zooniverse platform with machine learning. Here, we study volunteer proposals for new glitch classes, discussing links between these glitches and the state of the detectors, and examining how new glitch classes pose a challenge for machine-learning classification. Our results demonstrate how Zooniverse empowers non-experts to make discoveries, and the importance of monitoring changes in data quality in the LIGO detectors.

gr-qc↗

arXiv2025

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.

astro-ph.IM↗

arXiv2021

Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning

The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this linked to different types of ground motion. Reclassification of data based on the updated model finds that 27 % of all transient noise at LIGO Livingston belongs to the new glitch class, making it the most frequent source of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.

gr-qc↗

arXiv2016

Early Advanced LIGO binary neutron-star sky localization and parameter estimation

2015 will see the first observations of Advanced LIGO and the start of the gravitational-wave (GW) advanced-detector era. One of the most promising sources for ground-based GW detectors are binary neutron-star (BNS) coalescences. In order to use any detections for astrophysics, we must understand the capabilities of our parameter-estimation analysis. By simulating the GWs from an astrophysically motivated population of BNSs, we examine the accuracy of parameter inferences in the early advanced-detector era. We find that sky location, which is important for electromagnetic follow-up, can be determined rapidly (~5 s), but that sky areas may be hundreds of square degrees. The degeneracy between component mass and spin means there is significant uncertainty for measurements of the individual masses and spins; however, the chirp mass is well measured (typically better than 0.1%).

astro-ph.HE↗