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.
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References
Abbott, B.P., et al.: Observation of gravitational waves from a binary black hole merger. J. Astrophys. Phys. Rev. Lett. 116(6), 061102 (2016)
Abbott, B.P., et al.: Properties of the binary black hole merger GW150914. J. Astrophys. Phys. Rev. Lett. 116(24), 241102 (2016)
Devine, C., Etienne, Z.B., McWilliams, S.T.: Optimizing spinning time-domain gravitational waveforms for advanced LIGO data analysis. Class. Quantum Gravity 33(12), 125025 (2016)
Berti, E., et al.: Inspiral, merger, and ringdown of unequal mass black hole binaries: a multipolar analysis. Phys. Rev. D 76(6), 064034 (2007)
Martynov, D.V., et al.: Sensitivity of the advanced LIGO detectors at the beginning of gravitational wave astronomy. Phys. Rev. D 93(11), 112004 (2016)
Berti, E.: The first sounds of merging black holes. arXiv preprint arXiv:1602.04476 (2016)
Khan, S., et al.: Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era. Phys. Rev. D 93(4), 044007 (2016)
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Agrawal, S., Aedula, R., Surya, D.S.R. (2020). Machine Learning Based Analysis of Gravitational Waves. In: Saha, S., Nagaraj, N., Tripathi, S. (eds) Modeling, Machine Learning and Astronomy. MMLA 2019. Communications in Computer and Information Science, vol 1290. Springer, Singapore. https://doi.org/10.1007/978-981-33-6463-9_13
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DOI: https://doi.org/10.1007/978-981-33-6463-9_13
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