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LigoDV-web: Providing easy, secure and universal access to a large distributed scientific data store for the LIGO Scientific Collaboration

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arxiv 1611.01089 v1 pith:PPYPG6H7 submitted 2016-11-03 astro-ph.IM gr-qc

classification astro-ph.IMgr-qc
keywords dataligocollaborationgravitational-waveligodv-webaccessincludingdistributed
verification ladder T0 review T1 audit T2 compute T3 formal

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Gravitational-wave observatories around the world, including the Laser Interferometer Gravitational-wave Observatory (LIGO), record a large volume of gravitational-wave output data and auxiliary data about the instruments and their environments. These data are stored at the observatory sites and distributed to computing clusters for data analysis. LigoDV-web is a web-based data viewer that provides access to data recorded at the LIGO Hanford, LIGO Livingston and GEO600 observatories, and the 40m prototype interferometer at Caltech. The challenge addressed by this project is to provide meaningful visualizations of small data sets to anyone in the collaboration in a fast, secure and reliable manner with minimal software, hardware and training required of the end users. LigoDV-web is implemented as a Java Enterprise Application, with Shibboleth Single Sign On for authentication and authorization and a proprietary network protocol used for data access on the back end. Collaboration members with proper credentials can request data be displayed in any of several general formats from any Internet appliance that supports a modern browser with Javascript and minimal HTML5 support, including personal computers, smartphones, and tablets. To date 634 unique users have visited the LigoDV-web website in a total of 33,861 sessions and generated a total of 139,875 plots. This infrastructure has been helpful in many analyses within the collaboration including follow-up of the data surrounding the first gravitational-wave events observed by LIGO in 2015.

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  1. GSpyNetTree-O4: an event validation tool used in the fourth LIGO-Virgo-KAGRA observing run

    gr-qc 2026-07 conditional novelty 6.0 of 10

    An updated convolutional-neural-network classifier for LIGO data identifies glitches near gravitational-wave candidates in 95–98% of test cases and can flag a glitch and a gravitational-wave signal in the same spectrogram.

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