Unveiling Cosmic Enigmas: Fast Radio Bursts Analysis Using Machine Learning and Convolutional Neural Networks
Keywords:
Fast Radio Burst, Deep Learning, Machine Learning, Space Telescope, Convolutional Neural Network, Neural Network, Random Forest, Radio Frequency Interference, Transfer Learning, Computer Vision, Incremental Learning, LogReg, Decision Tree, Extra-Tree, Random ForestLGBMAbstract
Universe has many mysteries like Pulsars, dying stars, supernovae, and fast radio bursts (FRB) are known as astronomical phenomena or events, which can be recognized by using different ML and DL approaches. Here one of the mysteries of fast radio busts events was discovered by using machine learning and convolution neural network with published data from the voyager radio telescope at frequencies from 8418.457MHz to 8421.38671 MHz. Also the analysis of different algorithm like CNN, LogReg, Decision Tree, Extra-Tree, Random Forest and LGBM to recognized radio emit from miscellaneous light across the electromagnetic spectrum and due to remonstrance advances in radio astronomy, the study of emitted objects in radio waves is the biggest scope of the study, which will be helping us to reveal the mysteries of the Universe.
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D. R. Lorimer et al. A Bright Millisecond Radio Burst of Extragalactic Origin.Science318,777-780(2007)
Evan Keane and Emily Petroff Fast Radio Bursts Parkes Data https://researchdata.edu.au/fast-radio-bursts- parkes/468266
Weishan Zhu and Long-Long Feng 2021 ApJ 906 95
Bjorn Jasper R Raquel, Tetsuya Hashimoto, Tomotsugu Goto, Bo Han Chen, Yuri Uno, Tiger Yu-Yang Hsiao, Seong Jin Kim, Simon C-C Ho, Machine learning classification of repeating FRBs from FRB 121102, Monthly Notices of the Royal Astronomical Society, Volume 524, Issue 2, September 2023, Pages 1668–1691, https://doi.org/10.1093/mnras/stad1942
Z. Li, C. Yu, J. Xiao, M. Long, C. Cui, Detection of radio frequency interference using an improved generative adversarial network, Astronomy and Computing, Volume 36, 2021, 100482, ISSN 2213-1337, ttps://doi.org/10.1016/j.ascom.2021.100482.
V. U. Rathod and S. V. Gumaste, “Role of Deep Learning in Mobile Ad-hoc Networks”, IJRITCC, vol. 10, no. 2s, pp. 237–246, Dec. 2022.
N. P. Sable, V. U. Rathod, P. N. Mahalle, and P. N. Railkar, “An Efficient and Reliable Data Transmission Service using Network Coding Algorithms in Peer-to-Peer Network”, IJRITCC, vol. 10, no. 1s, pp. 144–154, Dec. 2022.
N. P. Sable, R. Sonkamble, V. U. Rathod, S. Shirke, J. Y. Deshmukh, and G. T. Chavan, “Web3 Chain Authentication and Authorization Security Standard (CAA)”, IJRITCC, vol. 11, no. 5, pp. 70–76, May 2023.
Vijay U. Rathod* & Shyamrao V. Gumaste, “Effect Of Deep Channel To Improve Performance On Mobile Ad-Hoc Networks”, J. Optoelectron. Laser, vol. 41, no. 7, pp. 754–756, Jul. 2022.
Rathod, V.U. and Gumaste, S.V., 2022. Role of Neural Network in Mobile Ad Hoc Networks for Mobility Prediction. International Journal of Communication Networks and Information Security, 14(1s), pp.153-166.
Y. Mali, “A Comparative Analysis of Machine Learning Models for Soil Health Prediction and Crop Selection”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 11, no. 10s, pp. 811–828, Aug. 2023.
N. P. Sable, V. U. Rathod, M. D. Salunke, H. B. Jadhav, R. S. Tambe, and S. R. Kothavle, “Enhancing Routing Performance in Software-Defined Wireless Sensor Networks through Reinforcement Learning”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), vol. 11, no. 10s, pp. 73–83, Aug. 2023.
M. Chen, H. Deng, F. Wang and K. Ji, "A Survey on File Format for Data Storage in Radio Astronomy," 2013 6th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Shenyang, China, 2013, pp. 324-327, doi: 10.1109/ICINIS.2013.90.
Zeguendry A, Jarir Z, Quafafou M. Quantum Machine Learning: A Review and Case Studies. Entropy. 2023; 25(2):287. https://doi.org/10.3390/e25020287
Petroff, E., Hessels, J.W.T. & Lorimer, D.R. Fast radio bursts. Astron Astrophys Rev 27, 4 (2019). https://doi.org/10.1007/s00159-019-0116-6
Ben Margalit, Paz Beniamini, Navin Sridhar, and Brian D. Metzger Implications of a Fast Radio Burst from a Galactic Magnetar, 2020 ApJL 899 L27
Anthony L. Piro and B. M. Gaensler 2018 ApJ 861 150
C.M. C. Zhang, C. Wang, G. Hobbs, C. J. Russell, D. Li, S.-B. Zhang, S. Dai, J.-W. Wu1, Z.-C. Pan, W.-W. Zhu Applying saliency map analysis in searches for pulsars and fast radio bursts, 0-16 (22 May 2020)
Chen Wu, Oiwei” Ivy Wong, Lawrence Rudnick, Stanislav S. Shabala, Matthew J. Alger, Julie K. Banfield, Cheng Soon Ong, Sarah V. White, Avery F. Garon, Ray P. Norris, Heinz Andernach, Jean Tate Radio Galaxy Zoo “CLARAN” a deep learning classifier for radio Morphologies,0-22 (29 Oct 2018)
Liam Connor and Joeri van Leeuwen - Applying Deep Learning to Fast Radio Burst Classification, (8 Mar 2018)
Sable, N.P., Rathod, V.U. (2023). Rethinking Blockchain and Machine Learning for Resource-Constrained WSN. In: Neustein, A., Mahalle, P.N., Joshi, P., Shinde, G.R. (eds) AI, IoT, Big Data and Cloud Computing for Industry 4.0. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-29713-7_17.
N. P. Sable, V. U. Rathod, R. Sable and G. R. Shinde, "The Secure E-Wallet Powered by Blockchain and Distributed Ledger Technology," 2022 IEEE Pune Section International Conference (PuneCon), Pune, India, 2022, pp. 1-5, doi: 10.1109/PuneCon55413.2022.10014893.
V. U. Rathod and S. V. Gumaste, "Role of Routing Protocol in Mobile Ad-Hoc Network for Performance of Mobility Models," 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India, 2023, pp. 1-6, doi: 10.1109/I2CT57861.2023.10126390.
N. P. Sable, V. U. Rathod, P. N. Mahalle and D. R. Birari, "A Multiple Stage Deep Learning Model for NID in MANETs," 2022 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2022, pp. 1-6, doi: 10.1109/ESCI53509.2022.9758191.
N. P. Sable, M. D. Salunke, V. U. Rathod and P. Dhotre, "Network for Cross-Disease Attention to the Severity of Diabetic Macular Edema and Joint Retinopathy," 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2022, pp. 1-7, doi: 10.1109/SMARTGENCON56628.2022.10083936.
V. U. Rathod, N. P. Sable, N. N. Thorat and S. N. Ajani, "Deep Learning Techniques Using Lightweight Cryptography for IoT Based E-Healthcare System," 2023 3rd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2023, pp. 1-5, doi: 10.1109/CONIT59222.2023.10205808.
S-B Zhang, G Hobbs, S Dai, L Toomey, L Staveley-Smith, C J Russell, X-F Wu, A new fast radio burst in the data sets containing the Lorimer burst, Monthly Notices of the Royal Astronomical Society: Letters, Volume 484, Issue 1, March 2019, Pages L147–L150, https://doi.org/10.1093/mnrasl/slz023
Zhang, K.; Li, L.; Zhang, Z.; Li, Q.; Luo, J.; Jiang, M. The Statistical Similarity of Repeating and Non-Repeating Fast Radio Bursts. Universe 2022, 8, 355. https://doi.org/10.3390/universe8070355
Kiri L. Wagstaff, Benyang Tang, David R. Thompson, Shakeh Khudikyan, Jane Wyngaard, Adam T. Deller, Divya Palaniswamy, Steven J. Tingay, and Randall B. Wayth - A Machine Learning Classifier for Fast Radio Burst Detection at the VLBA, (28 Jun 2016)
Shorten, C., Khoshgoftaar, T.M. A survey on Image Data Augmentation for Deep Learning. J Big Data 6, 60 (2019). https://doi.org/10.1186/s40537-019-0197-0
Nanni, L., Paci, M., Brahnam, S. et al. Feature transforms for image data augmentation. Neural Comput & Applic 34, 22345–22356 (2022). https://doi.org/10.1007/s00521-022-07645-z
Jagtap, V., Pujeri, U. ., Parlewar, P. ., Landge, S. ., Ghorpade, A. ., Alshi, A. ., & Khutade, H. . (2023). Biometric Face Recognition System using Deep Dream and CNN. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 145–154. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3241
Yunfan Gerry Zhang et al 2018 ApJ 866 149
Liam Connor, Joeri van Leeuwen, Liam Connor and Joeri van Leeuwen Applying Deep Learning to Fast Radio Burst Classification, 8 Mar 2018
Devansh Agarwal, Kshitij Aggarwal, Sarah Burke-Spolaor, Duncan R Lorimer, Nathaniel Garver-Daniels, FETCH: A deep-learning based classifier for fast transient classification, Monthly Notices of the Royal Astronomical Society, Volume 497, Issue 2, September 2020, Pages 1661–1674, https://doi.org/10.1093/mnras/staa1856
Zhang, Y. G., Gajjar, V., Foster, G., Siemion, A., Cordes, J. M., Law, C. J., & Wang, Y. (2018). Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach [Journal Article]. The Astrophysical Journal, 866, 1–18.
Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8
Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN COMPUT. SCI. 2, 420 (2021). https://doi.org/10.1007/s42979-021-00815-1
Taye MM. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers. 2023; 12(5):91. https://doi.org/10.3390/computers12050091
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). https://doi.org/10.1007/s42979-021-00592-x
Sharma, S. ., Kumar, N. ., & Kaswan, K. S. . (2023). Hybrid Software Reliability Model for Big Fault Data and Selection of Best Optimizer Using an Estimation Accuracy Function . International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 26–37. https://doi.org/10.17762/ijritcc.v11i1.5984
Johnson, M., Williams, P., González, M., Hernandez, M., & Muñoz, S. Applying Machine Learning in Engineering Management: Challenges and Opportunities. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/90
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