HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW

  • Manit Rajendrakumar Patel The New Tulip International School
Keywords: artificial intelligence, machine learning, applications, astronomy, review

Abstract

Artificial intelligence (AI) is a discipline of computing that focuses mostly on transferring human intelligence and mental processes into machines that can assist humans in many ways. Machine learning (ML) is the approach of choice in AI for creating useful software for computer vision, speech recognition, natural language processing, robot control, and other applications. Some of the most common analyses of large, complicated and multidimensional data sets in astronomy can be performed by using ML methods. It can be used for automating observatory scheduling to increase the effective utilization and scientific return from telescopes. It is also used for image recognition, classification of galaxies and planet recognition. This paper offers an in-depth review of the evolution of artificial intelligence and the use of AI and ML in the field of astronomy, especially for data analysis, image recognition, astronomical scheduling, classification of galaxies and planet recognition. It adds to the existing literature on use of artificial intelligence for astronomical applications and is a useful resource for students and researchers.

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References

Almeida, J., Aguerri, J., Muñoz-Tuñón, C., & de Vicente, A. (2010). Automatic unsupervised classification of all Sloan digital sky survey data release 7 Galaxy Spectra. The Astrophysical Journal, 714(1), 487-504. doi: 10.1088/0004-637x/714/1/487

An, T. (2019). Science opportunities and challenges associated with SKA big data. Physics, Mechanics and Astronomy, 62(8). doi: 10.1007/s11433-018-9360-x

Angel, J., Wizinowich, P., Lloyd-Hart, M., & Sandler, D. (1990). Adaptive optics for array telescopes using neural-network techniques. Nature, 348(6298), 221-224. doi: 10.1038/348221a0

Baber, W. F. (1988). The arts of the natural: Herbert Simon and artificial intelligence. Public Administration Quarterly, 12(3), 329-347.

Ball, N. M., Brunner, R. J., Myers, A. D., & Tcheng, D. (2006). Robust machine learning applied to astronomical data sets. I. Star‐Galaxy Classification of the sloan digital sky survey DR3 using decision trees. The Astrophysical Journal, 650(1), 497–509. https://doi.org/10.1086/507440

Ball, N., & Brunner, R. (2010). Data mining and machine learning in astronomy. International Journal Of Modern Physics, 19(07), 1049-1106. doi: 10.1142/s0218271810017160

Ball, N., Loveday, J., Fukugita, M., Nakamura, O., Okamura, S., Brinkmann, J., & Brunner, R. (2004). Galaxy types in the Sloan Digital Sky Survey using supervised artificial neural networks. Monthly Notices Of The Royal Astronomical Society, 348(3), 1038-1046. doi: 10.1111/j.1365-2966.2004.07429.x

Banerji, M., Lahav, O., Lintott, C., Abdalla, F., Schawinski, K., & Bamford, S. et al. (2010). Galaxy Zoo: reproducing galaxy morphologies via machine learning. Monthly Notices Of The Royal Astronomical Society, 406(1), 342-353. doi: 10.1111/j.1365-2966.2010.16713.x

Baron, D. (2019). Machine Learning in Astronomy: a practical overview. arXiv: Instrumentation and Methods for Astrophysics.

Baron, D., & Poznanski, D. (2016). The weirdest SDSS galaxies: results from an outlier detection algorithm. Monthly Notices Of The Royal Astronomical Society, 465(4), 4530-4555. doi: 10.1093/mnras/stw3021

Bekki, K., Diaz, J., & Stanley, N. (2019). The AIverse project: Simulating, analyzing, and describing galaxies and star clusters with artificial intelligence. Astronomy and Computing, 28, 100286.

Bonaldi, A., An, T., Brüggen, M., Burkutean, S., Coelho, B., & Goodarzi, H. et al. (2020). Square Kilometre Array Science Data Challenge 1: analysis and results. Monthly Notices Of The Royal Astronomical Society, 500(3), 3821-3837. doi: 10.1093/mnras/staa3023

Borne, K. (2008). A machine learning classification broker for the LSST transient database. Astronomische Nachrichten, 329(3), 255-258. doi: 10.1002/asna.200710946

Byrd, D. (2022). Astronomers report success with machine deep learning. EarthSky. https://earthsky.org/space/machine-deep-learning-2-astronomy-studies

Carrasco-Davis, R., Cabrera-Vives, G., Förster, F., Estévez, P., Huijse, P., & Protopapas, P. et al. (2019). Deep Learning for Image Sequence Classification of Astronomical Events. Publications Of The Astronomical Society Of The Pacific, 131(1004), 108006. doi: 10.1088/1538-3873/aaef12

Castelvecchi, D. (2016, September 27). Deep learning boosts google translate tool. Nature News. https://www.nature.com/articles/nature.2016.20696

De Spiegeleire, S., Maas, M., & Sweijs, T. (2017). AI – today and tomorrow. In Artificial Intelligence And The Future Of Defense: Strategic Implications For Small- And Medium-Sized Force Providers (pp. 43–59). Hague Centre for Strategic Studies. http://www.jstor.org/stable/resrep12564.8

Delli Veneri, M., Cavuoti, S., Brescia, M., Longo, G., & Riccio, G. (2019). Star formation rates for photometric samples of galaxies using machine learning methods. Monthly Notices Of The Royal Astronomical Society, 486(1), 1377-1391. doi: 10.1093/mnras/stz856

Dieleman, S., Willett, K., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices Of The Royal Astronomical Society, 450(2), 1441-1459. doi: 10.1093/mnras/stv632

Djorgovski, S., Brunner, R., Gal, R., De Carvalho, R., Odewahn, S., & Mahabal, A. et al. (2002). The Digital Palomar Observatory Sky Survey (DPOSS): General Description and the Public Data Release. Bulletin Of The Astronomical Society Of Brazil, 34(2), 743.

Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. (2018). Improving galaxy morphologies for SDSS with Deep Learning. Monthly Notices Of The Royal Astronomical Society, 476(3), 3661-3676. doi: 10.1093/mnras/sty338

Ertel, W. (2017). Introduction to Artificial Intelligence (2nd ed., p. 1). Springer Cham.

Floudas, C. A., and Lin, X. (2005). Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications. Annals of Operations Research 139(3), 131–162.

Fluke, C., Hegarty, S., & MacMahon, C. (2020). Understanding the human in the design of cyber-human discovery systems for data-driven astronomy. Astronomy And Computing, 33, 100423. doi: 10.1016/j.ascom.2020.10042

French, R. M. (2000). The Turing Test: the first 50 years. Trends in cognitive sciences, 4(3), 115-122

Gómez de Castro, & Yáñez, J. (2003). Optimization of telescope scheduling. Astronomy and Astrophysics, 403(1), 357-367. doi: 10.1051/0004-6361:20030319

Havlík, V. (2019). The naturalness of artificial intelligence from the evolutionary perspective. AI & Society, 34(3), 889–898.

Hobson, M., Graff, P., Feroz, F., and Lasenby, A. (2014). Machine-learning in astronomy. In A. F. Heavens, J.-L. Starck & A. Krone-Martins, (Eds.) Statistical Challenges in 21st Century Cosmology. Proceedings IAU Symposium No. 306, International Astronomical Union. doi:10.1017/S1743921314013672

Huertas-Company, M., Aguerri, J., Bernardi, M., Mei, S., & Sánchez Almeida, J. (2010). Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available Bayesian automated classification. Astronomy and Astrophysics, 525, A157. doi: 10.1051/0004-6361/201015735

Johnston, M. (1988). Artificial intelligence approaches to spacecraft scheduling. In Proceedings of ESA Workshop on Artificial Intelligence Applications for Space Projects, ESTEC (Noordwijk, Holland) (Vol. 5).

Johnston, M. and Miller, G. (1989). Artificial Intelligence Approaches to Astronomical Observation Scheduling. In Gesù, V., Scarsi, L., Crane, P., Friedman, J. H., Levialdi, S. and Maccarone, M. C. (eds.) Data Analysis in Astronomy III, pp 205–214, Springer.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, Perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415

Kremer, J., Stensbo-Smidt, K., Gieseke, F., Pedersen, K., & Igel, C. (2017). Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems, 32(2), 16-22. doi: 10.1109/mis.2017.40

Kuminski, E., & Shamir, L. (2018). A hybrid approach to machine learning annotation of large galaxy image databases. Astronomy and Computing, 25, 257-269.

Lahav, O., Naim, A., Buta, R., Corwin, H., de Vaucouleurs, G., & Dressler, A. et al. (1995). Galaxies, Human Eyes, and Artificial Neural Networks. Science, 267(5199), 859-862. doi: 10.1126/science.267.5199.859

Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Expert and Novice Performance in Solving Physics Problems. Science, 208(4450), 1335-1342. doi: 10.1126/science.208.4450.1335

Lemmer, J., & Kanal, L. (1986). Preface. Uncertainty In Artificial Intelligence, v-vi. doi: 10.1016/b978-0-444-70058-2.50004-8

Linn, A. (2015). Microsoft Researchers Win ImageNet Computer Vision Challenge [Blog]. Retrieved from https://blogs.microsoft.com/next/2015/12/10/microsoft-researchers-win-imagenet-computer-visionchallenge

Long, J. P., & Souza, R. S. (2017). Statistical methods in astronomy. Wiley StatsRef: Statistics Reference Online, 1–9. https://doi.org/10.1002/9781118445112.stat07996

Longo, G., Merényi, E., & Tiňo, P. (2019). Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics. Publications of The Astronomical Society of The Pacific, 131(1004), 100101. doi: 10.1088/1538-3873/ab2743

MacKay, D. J. C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge.

McDermott, J. P. (1980, August). RI: an Expert in the Computer Systems Domain. In AAAI (Vol. 1, pp. 269-271).

Mora, M., & Solar, M. (2010). A Survey on the Dynamic Scheduling Problem in Astronomical Observations. IFIP Advances in Information and Communication Technology, 331, 111-120.

Meher, S. K. and Panda, G. (2021). Deep learning in astronomy: a tutorial perspective. The European Physical Journal, 230(10), 2285 – 2317.

Metz, C. (2016, November 29). Google's hand-fed ai now gives answers, not just search results. Wired. https://www.wired.com/2016/11/googles-search-engine-can-now-answer-questions-human-help/

Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In Fundamental issues of artificial intelligence (pp. 555-572). Springer, Cham.

Nair, P., & Abraham, R. (2010). A catalog of detailed visual morphological classifications for 14,034 galaxies in the Sloan digital sky survey. The Astrophysical Journal Supplement Series, 186(2), 427-456. doi: 10.1088/0067-0049/186/2/427

Newell, A. (1969). A Step toward the Understanding of Information Processes: Perceptrons . An Introduction to Computational Geometry. Marvin Minsky and Seymour Papert. M.I.T. Press, Cambridge, Massachusetts.

Nolan, L., Harva, M., Kaban, A., & Raychaudhury, S. (2006). A data-driven Bayesian approach for finding young stellar populations in early-type galaxies from their ultraviolet-optical spectra. Monthly Notices Of The Royal Astronomical Society, 366(1), 321-338. doi: 10.1111/j.1365-2966.2005.09868.x

Nolan, L., Raychaudhury, S., & Kaban, A. (2007). Young stellar populations in early-type galaxies in the Sloan Digital Sky Survey. Monthly Notices Of The Royal Astronomical Society, 375(1), 381-387. doi: 10.1111/j.1365-2966.2006.11326.x

Norris, R. P., Salvato, M., Longo, G., Brescia, M., Budavari, T., Carliles, S., ... & Zinn, P. (2019). A comparison of photometric redshift techniques for large radio surveys. Publications of the Astronomical Society of the Pacific, 131(1004), 108004.

Norris, R., Hopkins, A., Afonso, J., Brown, S., Condon, J., & Dunne, L. et al. (2011). EMU: Evolutionary Map of the Universe. Publications Of The Astronomical Society Of Australia, 28(3), 215-248. doi: 10.1071/as11021

Nyman, L. Å., Andreani, P., Hibbard, J., & Okumura, S. K. (2010, July). ALMA science operations. In Observatory Operations: Strategies, Processes, and Systems III (Vol. 7737, pp. 94-100). SPIE.

Pearson, K. A., Palafox, L., & Griffith, C. A. (2018). Searching for exoplanets using artificial intelligence. Monthly Notices of the Royal Astronomical Society, 474(1), 478-491.

Pesenson, M., Pesenson, I., & McCollum, B. (2010). The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch. Advances In Astronomy, 1-16. doi: 10.1155/2010/350891

Phan, T., Feld, S., & Linnhoff-Popien, C. (2020). Artificial Intelligence – the new Revolutionary Evolution. Digitale Welt.

Pruthi, N. (2019). Artificial Intelligence in Astronomy. International Journal for Research in Applied Science & Engineering Technology, 7(12), 904-906.

Polsterer, K. L., Gieseke, F., Igel, C., & Goto, T. (2013). Improving the performance of photometric regression models via massive parallel feature selection. In Proceedings of the 23rd Annual Astronomical Data Analysis Software & Systems conference (ADASS).

Ravanbakhsh, S., Lanusse, F., Mandelbaum, R., Schneider, J., & Poczos, B. (2017). Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. Proceedings Of The AAAI Conference On Artificial Intelligence, 31(1). doi: 10.1609/aaai.v31i1.10755

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. doi: 10.1037/h0042519

Russel, S., & Norvig, P. (2014). Artificial Intelligence: A Modern Approach (3rd ed.). [S.l.]: Pearson Education.

Ivan Sanchez, I., Mitchell, J., and Riedel, S. (2018). Behavior analysis of NLI models: Uncovering the influence of three factors on robustness. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 1975–1985.

Searls, D. (2007). A View from the Dark Side. PLOS Computational Biology, 3(6), e105. doi: 10.1371/journal.pcbi.0030105

Sen, S., Agarwal, S., Chakraborty, P. and Singh, K. (2022). Astronomical big data processing using machine learning: A comprehensive review. Experimental Astronomy, 53(1), 1-43.

Silburt, A., Ali-Dib, M., Zhu, C., Jackson, A., Valencia, D., & Kissin, Y. et al. (2019). Lunar crater identification via deep learning. Icarus, 317, 27-38. doi: 10.1016/j.icarus.2018.06.022

Solar, M., Michelon, P., Avarias, J., & Garcés, M. (2016). A scheduling model for astronomy. Astronomy and Computing, 15, 90-104.

Spotts, P. (2010, May 21). New telescopes could revolutionize astronomy, but at what price? The Christian Science Monitor. https://www.csmonitor.com/USA/Society/2010/0521/New-telescopes-could-revolutionize-astronomy-but-at-what-price

Tagliaferri R., L. G., D’Argenio B., Incoronato A. (2003). Neural Networks, 16, 297.

a. Tino, P., and Raychaudhury, S. (2012). Computational Intelligence in Astronomy- A Win-Win Situation. A.-H. Dediu, C. Martin-Vide, and B. Truthe (Eds.) In Lecture Notes in Computer Science, 7505, p. 57-71.

Tucker, L. (2022). Artificial Intelligence Now Being Used for New Discoveries in Astronomy. Make Tech Easier. https://www.maketecheasier.com/artificial-intelligence-discoveries-astronomy/

Turban, E., & Watkins, P. R. (1986). Integrating Expert Systems and Decision Support Systems. MIS Quarterly, 10(2), 121–136. https://doi.org/10.2307/249031

Vasconcellos, E., de Carvalho, R., Gal, R., LaBarbera, F., Capelato, H., & Frago Campos Velho, H. et al. (2011). Decision tree classifiers for star/galaxy separation. The Astronomical Journal, 141(6), 189. doi: 10.1088/0004-6256/141/6/189

Vasista, K. (2022). Evolution of AI Design Models. Central Asian Journal Of Theoretical And Applied Sciences, 3(3), 1-2.

Wadadekar, Y. (2005). Estimating Photometric Redshifts Using Support Vector Machines. Publications Of The Astronomical Society Of The Pacific, 117(827), 79-85. doi: 10.1086/427710

Wang, K., Guo, P., Yu, F., Duan, L., Wang, Y., & Du, H. (2018). Computational Intelligence in Astronomy: A Survey. International Journal Of Computational Intelligence Systems, 11(1), 575-590. doi: 10.2991/ijcis.11.1.43

Way, M. J., Scargle, J. D., Ali, K. M., and Srivastava, A. N. (2012). Advances in Machine Learning and Data Mining for Astronomy (CRC Press).

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M. L., Stolcke, A., Yu, D., & Zweig, G. (2017). Toward human parity in conversational speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(12), 2410–2423. https://doi.org/10.1109/taslp.2017.2756440

York, D., Adelman, J., Andeson Jr., J., Anderson, S., Annis, J., & Bahcall, N. et al. (2000). The sloan digital sky survey: Technical summary. The Astronomical Journal, 120(3), 1579.

Zhai, Y., Yan, J., Zhang, H., & Lu, W. (2020). Tracing the evolution of AI: conceptualization of artificial intelligence in mass media discourse. Information Discovery And Delivery, 48(3), 137-149. doi: 10.1108/idd-01-2020-0007

Published
2022-11-16
How to Cite
Patel, M. R. (2022). HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW. IJRDO -Journal of Computer Science Engineering, 8(11), 1-12. https://doi.org/10.53555/cse.v8i11.5427