Jove
Visualize
Contact Us

Related Concept Videos

Detection of Black Holes01:10

Detection of Black Holes

2.6K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.6K
Neural Circuits01:25

Neural Circuits

3.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.2K
Deconvolution01:20

Deconvolution

685
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
685
Force Classification01:22

Force Classification

2.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.6K
Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Convolution Properties II01:17

Convolution Properties II

652
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
652

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Particle Swarm Optimisation Applied to the Direct Aperture Optimisation Problem in Radiation Therapy.

Cancers·2023
Same author

Exploring the Potential of Machine Learning for the Diagnosis of Balance Disorders Based on Centre of Pressure Analyses.

Sensors (Basel, Switzerland)·2022
Same author

Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis.

Entropy (Basel, Switzerland)·2020
Same author

Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis.

Entropy (Basel, Switzerland)·2020
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K

LIGO Core-Collapse Supernova Detection Using Convolutional Neural Networks.

Zhicheng Pan1, El Mehdi Zahraoui2, Patricio Maturana-Russel2,3

  • 1Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

Convolutional neural networks (CNNs) improve gravitational wave (GW) detection from core-collapse supernovae (CCSNe). CNNs trained on STFT spectrograms show superior performance for low signal-to-noise ratios, crucial for understanding massive star explosions.

Keywords:
CNNQ-transformaLIGOcore-collapse supernovae

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.8K

Related Experiment Videos

Last Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.2K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.8K

Area of Science:

  • Astronomy and Astrophysics
  • Gravitational Wave Detection
  • Computational Astrophysics

Background:

  • Core-collapse supernovae (CCSNe) are key targets for gravitational wave (GW) astronomy.
  • Understanding CCSNe explosion mechanisms requires sensitive GW detection methods.
  • Current detection techniques face challenges with low signal-to-noise ratio (SNR) events.

Purpose of the Study:

  • To investigate the efficacy of convolutional neural networks (CNNs) for enhancing CCSNe GW signal detection.
  • To compare the performance of CNNs trained on different time-frequency representations.
  • To improve the identification of faint CCSNe GW signals.

Main Methods:

  • Simulated CCSNe GW signals and Advanced LIGO noise were used for training data.
  • Two time-frequency analysis techniques, Short-Time Fourier Transform (STFT) and Q-transform (QT), were employed to generate spectrograms.
  • Two independent CNNs were trained on STFT and QT spectrograms, respectively.

Main Results:

  • Both CNNs achieved near 100% true positive rates for CCSNe GW events with SNR > 0.5.
  • The CNN trained on STFT spectrograms outperformed the QT-based CNN for SNRs below 0.5.
  • CNNs effectively detect CCSNe signals via their time-frequency representations.

Conclusions:

  • CNNs offer a promising approach to enhance CCSNe GW detection.
  • STFT-based CNNs demonstrate superior sensitivity for weaker GW signals.
  • These findings contribute to advancing the search for and analysis of CCSNe GWs.