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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Machine Learning Based Analysis of Gravitational Waves
verfasst von : Surbhi Agrawal, Rahul Aedula, D. S. Rahul Surya
Erschienen in: Modeling, Machine Learning and Astronomy
Verlag: Springer Singapore
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Abstract
Gravitational waves has been a serious subject of study in the modern day astrophysics. Where on one end the strain produced by gravitational waves on matter could be practically studied by Laser Interferometers such as LIGO, the strain generated by celestial bodies on the other end a priori obtained by numerical relativity in the form of waveforms. It is often the case that these waveforms are only used to study the properties of black holes. This article tries to extrapolate such methodologies to weaker celestial bodies for the primary purpose of adding a new dimensionality in the prudent realm of possibilities. There is a necessity to approach such studies from a statistical perspective. Utilizing the combination of Statistical and Machine Learning tools not only assist in analyzing data effectively but also aid in creating a generalized computational model.