scholarly journals Implementation of barycentric resampling for continuous wave searches in gravitational wave data

2010 ◽  
Vol 81 (8) ◽  
Author(s):  
Pinkesh Patel ◽  
Xavier Siemens ◽  
Rejean Dupuis ◽  
Joseph Betzwieser
Keyword(s):  
Gravitational Wave ◽  
Continuous Wave ◽  
Wave Data

scholarly journals Noise reduction in gravitational-wave data via deep learning

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Rich Ormiston ◽  
Tri Nguyen ◽  
Michael Coughlin ◽  
Rana X. Adhikari ◽  
Erik Katsavounidis
Keyword(s):  
Deep Learning ◽  
Gravitational Wave ◽  
Noise Reduction ◽  
Wave Data

scholarly journals Improving the sensitivity of a search for coalescing binary black holes with nonprecessing spins in gravitational wave data

2014 ◽  
Vol 89 (2) ◽  
Author(s):  
Stephen Privitera ◽  
Satyanarayan R. P. Mohapatra ◽  
Parameswaran Ajith ◽  
Kipp Cannon ◽  
Nickolas Fotopoulos ◽  
...  
Keyword(s):  
Black Holes ◽  
Gravitational Wave ◽  
Binary Black Holes ◽  
Wave Data

scholarly journals Maximum Entropy for Gravitational Wave Data Analysis: Inferring the Physical Parameters of Core‐Collapse Supernovae

2008 ◽  
Vol 678 (2) ◽  
pp. 1142-1157 ◽  
Author(s):  
T. Z. Summerscales ◽  
Adam Burrows ◽  
Lee Samuel Finn ◽  
Christian D. Ott
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Maximum Entropy ◽  
Physical Parameters ◽  
Core Collapse ◽  
Wave Data

scholarly journals Low significance of evidence for black hole echoes in gravitational wave data

2018 ◽  
Vol 97 (12) ◽  
Author(s):  
Julian Westerweck ◽  
Alex B. Nielsen ◽  
Ofek Fischer-Birnholtz ◽  
Miriam Cabero ◽  
Collin Capano ◽  
...  
Keyword(s):  
Black Hole ◽  
Gravitational Wave ◽  
Wave Data

Mass Determination of Black Holes from Gravitational Wave Data Using Neural Networks

2021 ◽  
Author(s):  
Blenda Úlima Rodrigues Cesar Guedes ◽  
Antonio de Pádua Santos ◽  
Tiago Alessandro Espínola Ferreira
Keyword(s):  
Neural Network ◽  
Black Holes ◽  
Gravitational Wave ◽  
Gravitational Waves ◽  
Albert Einstein ◽  
Wave Data ◽  
Real World Problem ◽  
Simple Neural Network ◽  
Physical Features ◽  
Simple Equations

Gravitational waves were predicted by Albert Einstein more than a century ago, but only in 2015, the Laser Interferometer Gravitational-Wave Observatory (LIGO) was able to detect them. The gravitational wave phenomenon can be compared to spreading water from a lake after a stone has been thrown into it. Here, gravitational wave generation comes from an astronomical binary system formed by black holes or neutron stars. However, unlike the water, the amplitude of those gravitational waves is on a scale smaller than a proton’s size. Despite this, we can describe it with simple equations in a phenomenological way. We can model those waves on a regular computer using post Newtonian physics. Here we were able to generate gravitational waves from computational simulations and make its data analyze. When a gravitational wave is detected in the real-world problem, there is a great interest in establishing the physical features of the astronomical bodies involved in the process. In this way, we propose applying a simple neural network to receive the gravitational wave data and infer information about the astronomical bodies’ mass. The experimental results show that a simple neural network can extract mass information from the gravitational wave data. The recognition process proposed is much more straightforward than the complex computation based on numerical relativity for gravitational wave data analysis.

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