scholarly journals Gravitational wave signatures ofab initiotwo-dimensional core collapse supernova explosion models for12–25  M⊙stars

2015 ◽  
Vol 92 (8) ◽  
Author(s):  
Konstantin N. Yakunin ◽  
Anthony Mezzacappa ◽  
Pedro Marronetti ◽  
Shin’ichirou Yoshida ◽  
Stephen W. Bruenn ◽  
...  
Keyword(s):  
Gravitational Wave ◽  
Supernova Explosion ◽  
Core Collapse ◽  
Core Collapse Supernova ◽  
M Stars

scholarly journals Gravitational-wave signal of a core-collapse supernova explosion of a 15 M⊙ star

2020 ◽  
Vol 102 (2) ◽  
Author(s):  
Anthony Mezzacappa ◽  
Pedro Marronetti ◽  
Ryan E. Landfield ◽  
Eric J. Lentz ◽  
Konstantin N. Yakunin ◽  
...  
Keyword(s):  
Gravitational Wave ◽  
Supernova Explosion ◽  
Core Collapse ◽  
Wave Signal ◽  
Core Collapse Supernova

scholarly journals Core-Collapse supernova gravitational-wave search and deep learning classification

2020 ◽  
Vol 1 (2) ◽  
pp. 025014 ◽  
Author(s):  
Alberto Iess ◽  
Elena Cuoco ◽  
Filip Morawski ◽  
Jade Powell
Keyword(s):  
Deep Learning ◽  
Gravitational Wave ◽  
Core Collapse ◽  
Core Collapse Supernova

scholarly journals A successful 3D core-collapse supernova explosion model

2018 ◽  
Vol 482 (1) ◽  
pp. 351-369 ◽  
Author(s):  
David Vartanyan ◽  
Adam Burrows ◽  
David Radice ◽  
M Aaron Skinner ◽  
Joshua Dolence
Keyword(s):  
Supernova Explosion ◽  
Core Collapse ◽  
Core Collapse Supernova

scholarly journals DENSE IRON EJECTA AND CORE-COLLAPSE SUPERNOVA EXPLOSION IN THE YOUNG SUPERNOVA REMNANT G11.2-0.3

2009 ◽  
Vol 703 (1) ◽  
pp. L81-L85 ◽  
Author(s):  
Dae-Sik Moon ◽  
Bon-Chul Koo ◽  
Ho-Gyu Lee ◽  
Keith Matthews ◽  
Jae-Joon Lee ◽  
...  
Keyword(s):  
Supernova Remnant ◽  
Supernova Explosion ◽  
Core Collapse ◽  
Core Collapse Supernova ◽  
Young Supernova Remnant

scholarly journals NEURAL NETWORK AIDED GLITCH-BURST DISCRIMINATION AND GLITCH CLASSIFICATION

2013 ◽  
Vol 24 (11) ◽  
pp. 1350084 ◽  
Author(s):  
SALVATORE RAMPONE ◽  
VINCENZO PIERRO ◽  
LUIGI TROIANO ◽  
INNOCENZO M. PINTO
Keyword(s):  
Neural Network ◽  
Gravitational Wave ◽  
Practical Interest ◽  
Principal Component ◽  
Core Collapse ◽  
Self Organizing Map ◽  
Single Node ◽  
Core Collapse Supernova ◽  
Entry Points ◽  
Single Detector

We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts (glitches), in the data of a single detector. The further classification of glitches into typical sets is explored. In order to provide a proof of concept, we use the core-collapse supernova waveform catalog produced by H. Dimmelmeier and co-Workers, and the data base of glitches observed in laser interferometer gravitational wave observatory (LIGO) data maintained by P. Saulson and co-Workers to construct datasets of (windowed) transient waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian) noise with different signal-to-noise ratios (SNR). Principal component analysis (PCA) is next implemented for reducing data dimensionality, yielding results consistent with, and extending those in the literature. Then, a multilayer perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset, and used to classify the transients as glitch or burst. A Self-Organizing Map (SOM) architecture is finally used to classify the glitches. The glitch/burst discrimination and glitch classification abilities are gauged in terms of the related truth tables. Preliminary results suggest that the approach is effective and robust throughout the SNR range of practical interest. Perspective applications pertain both to distributed (network, multisensor) detection of GWBs, where some intelligence at the single node level can be introduced, and instrument diagnostics/optimization, where spurious transients can be identified, classified and hopefully traced back to their entry points.

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