English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

$Context.$ Core-collapse supernovae (CCSNe) are expected to emitgravitational wave signals that could be detected by current and futuregeneration interferometers within the Milky Way and nearby galaxies. Thestochastic nature of the signal arising from CCSNe requires alternativedetection methods to matched filtering. $Aims.$ We aim to show the potential ofmachine learning (ML) for multi-label classification of different CCSNesimulated signals and noise transients using real data. We compared theperformance of 1D and 2D convolutional neural networks (CNNs) on single andmultiple detector data. For the first time, we tested multi-labelclassification also with long short-term memory (LSTM) networks. $Methods.$ Weapplied a search and classification procedure for CCSNe signals, using an eventtrigger generator, the Wavelet Detection Filter (WDF), coupled with ML. We usedtime series and time-frequency representations of the data as inputs to the MLmodels. To compute classification accuracies, we simultaneously injected, atdetectable distance of 1\,kpc, CCSN waveforms, obtained from recenthydrodynamical simulations of neutrino-driven core-collapse, ontointerferometer noise from the O2 LIGO and Virgo science run. $Results.$ Wecompared the performance of the three models on single detector data. We thenmerged the output of the models for single detector classification of noise andastrophysical transients, obtaining overall accuracies for LIGO ($\sim99\%$)and ($\sim80\%$) for Virgo. We extended our analysis to the multi-detector caseusing triggers coincident among the three ITFs and achieved an accuracy of$\sim98\%$.

LSTM and CNN application for core-collapse supernova search in gravitational wave real data