ARCHIVES
Original Article
Machine Learning-Based Detection of Gravitational Waves from Gamma-Ray Burst Data using Swift BAT
Deepak Kumar Nalwaya1
Manju Mandot2
1 Professor, Research Scholar, Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University), Udaipur, Rajasthan, India. 2 Director, DCS & IT, Janardan Rai Nagar Rajasthan Vidyapeeth (Deemed to be University), Udaipur, Rajasthan, India
Published Online: January-April 2026
Pages: 515-523
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501059
References
1. B. P. Abbott et al., “Observation of Gravitational Waves from a Binary Black Hole Merger,” Physical Review Letters, vol. 116, no. 6,
2016.
2. B. P. Abbott et al., “GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral,” Physical Review Letters, 2017.
3. J. Aasi et al., “Advanced LIGO,” Classical and Quantum Gravity, vol. 32, 2015.
4. LIGO Scientific Collaboration, “LIGO Data and Scientific Results,” 2016–2024.
5. NASA, “Swift Mission Overview and Data Archive,” 2020.
6. Swift BAT Team, “BAT Instrument and GRB Observations,” Astrophysical Journal, 2005.
7. N. Gehrels et al., “The Swift Gamma-Ray Burst Mission,” Astrophysical Journal, vol. 611, 2004.
8. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
9. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
10. K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
11. J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Pearson, 2007.
12. A. Oppenheim and R. Schafer, Signals and Systems, Prentice Hall, 1996.
13. S. Mallat, “A Wavelet Tour of Signal Processing,” Academic Press, 1999.
14. D. George and E. A. Huerta, “Deep Neural Networks to Enable Real-time Multimessenger Astrophysics,” Physical Review D, 2018.
15. D. George and E. Huerta, “Deep Learning for Real-time Gravitational Wave Detection,” Physics Letters B, 2017.
16. H. Gabbard et al., “Matching Matched Filtering with Deep Networks for Gravitational Wave Detection,” Physical Review Letters, 2018.
17. W. Wei and E. A. Huerta, “Gravitational Wave Detection Using Machine Learning,” Nature Astronomy, 2020.
18. A. Krizhevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks,” NeurIPS, 2012.
19. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, 1997.
20. L. Breiman, “Random Forests,” Machine Learning, vol. 45, 2001.
21. C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, 1995.
22. P. Meszaros, “Gamma-Ray Bursts,” Reports on Progress in Physics, 2006.
23. B. Zhang and P. Meszaros, “Gamma-Ray Burst Physics,” International Journal of Modern Physics A, 2004.
24. E. Nakar, “Short-Hard Gamma-Ray Bursts,” Physics Reports, 2007.
25. B. F. Schutz, “Networks of Gravitational Wave Detectors and Three Figures of Merit,” Classical and Quantum Gravity, 2011.
26. J. Veitch et al., “Parameter Estimation for Compact Binaries,” Physical Review D, 2015.
27. M. Maggiore, Gravitational Waves: Theory and Experiments, Oxford University Press, 2008.
28. S. Chandrasekhar, The Mathematical Theory of Black Holes, Oxford, 1998.
29. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2009.
30. F. Chollet, Deep Learning with Python, Manning, 2017.
31. J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, 2007.
32. HEASoft Documentation, NASA, 2023.
33. OriginLab Corporation, “OriginPro 2025 User Guide,” 2025.
2016.
2. B. P. Abbott et al., “GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral,” Physical Review Letters, 2017.
3. J. Aasi et al., “Advanced LIGO,” Classical and Quantum Gravity, vol. 32, 2015.
4. LIGO Scientific Collaboration, “LIGO Data and Scientific Results,” 2016–2024.
5. NASA, “Swift Mission Overview and Data Archive,” 2020.
6. Swift BAT Team, “BAT Instrument and GRB Observations,” Astrophysical Journal, 2005.
7. N. Gehrels et al., “The Swift Gamma-Ray Burst Mission,” Astrophysical Journal, vol. 611, 2004.
8. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
9. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
10. K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
11. J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Pearson, 2007.
12. A. Oppenheim and R. Schafer, Signals and Systems, Prentice Hall, 1996.
13. S. Mallat, “A Wavelet Tour of Signal Processing,” Academic Press, 1999.
14. D. George and E. A. Huerta, “Deep Neural Networks to Enable Real-time Multimessenger Astrophysics,” Physical Review D, 2018.
15. D. George and E. Huerta, “Deep Learning for Real-time Gravitational Wave Detection,” Physics Letters B, 2017.
16. H. Gabbard et al., “Matching Matched Filtering with Deep Networks for Gravitational Wave Detection,” Physical Review Letters, 2018.
17. W. Wei and E. A. Huerta, “Gravitational Wave Detection Using Machine Learning,” Nature Astronomy, 2020.
18. A. Krizhevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks,” NeurIPS, 2012.
19. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, 1997.
20. L. Breiman, “Random Forests,” Machine Learning, vol. 45, 2001.
21. C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, 1995.
22. P. Meszaros, “Gamma-Ray Bursts,” Reports on Progress in Physics, 2006.
23. B. Zhang and P. Meszaros, “Gamma-Ray Burst Physics,” International Journal of Modern Physics A, 2004.
24. E. Nakar, “Short-Hard Gamma-Ray Bursts,” Physics Reports, 2007.
25. B. F. Schutz, “Networks of Gravitational Wave Detectors and Three Figures of Merit,” Classical and Quantum Gravity, 2011.
26. J. Veitch et al., “Parameter Estimation for Compact Binaries,” Physical Review D, 2015.
27. M. Maggiore, Gravitational Waves: Theory and Experiments, Oxford University Press, 2008.
28. S. Chandrasekhar, The Mathematical Theory of Black Holes, Oxford, 1998.
29. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2009.
30. F. Chollet, Deep Learning with Python, Manning, 2017.
31. J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, 2007.
32. HEASoft Documentation, NASA, 2023.
33. OriginLab Corporation, “OriginPro 2025 User Guide,” 2025.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
