On the Efficiency of Various Deep Transfer Learning Models in Glitch Waveform Detection in Gravitational-Wave Data

• URL: http://arxiv.org/abs/2107.01863v1
• Date: Mon, 5 Jul 2021 08:32:58 GMT
• Title: On the Efficiency of Various Deep Transfer Learning Models in Glitch Waveform Detection in Gravitational-Wave Data
• Authors: Reymond Mesuga and Brian James Bayanay
• Abstract summary: LIGO is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the LIGO community as glitches. The accuracy achieved by the said models are 98.98%, 98.35%, 97.56% and 94.73% respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LIGO is considered the most sensitive and complicated gravitational experiment ever built. Its main objective is to detect the gravitational wave from the strongest events in the universe by observing if the length of its 4-kilometer arms change by a distance 10,000 times smaller than the diameter of a proton. Due to its sensitivity, LIGO is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the LIGO community as glitches. The objective of this study is to evaluate the effeciency of various deep trasnfer learning models namely VGG19, ResNet50V2, VGG16 and ResNet101 to detect glitch waveform in gravitational wave data. The accuracy achieved by the said models are 98.98%, 98.35%, 97.56% and 94.73% respectively. Even though the models achieved fairly high accuracy, it is observed that all of the model suffered from the lack of data for certain classes which is the main concern found in the experiment

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