Statistical Data Retrieval Technique in Astronomy Computational Physics
Keywords:
Computational Physics, Statistical astronomy methods, Astronomy data
Abstract
Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.
References
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[2] S. Dere, M. Fatima, R. Jagtap, U. Inamdar, and N. Shardoor, “Anomalous Behavior Detection in Galaxies and Exoplanets using ML & DL Techniques,” presented at the 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021, pp. 938–947.
[3] J. Nouri, L. Zhang, L. Mannila, and E. Norén, “Development of computational thinking, digital competence and 21st century skills when learning programming in K-9,” Education Inquiry, vol. 11, no. 1, pp. 1–17, 2020.
[4] B. Bean et al., “CASA, Common Astronomy Software Applications for Radio Astronomy,” Publications of the Astronomical Society of the Pacific, vol. 134, no. 1041, p. 114501, 2022.
[5] B. Herkommer, “Measuring the energy spectra of unknown samples using coherent control of the complex phase of X-rays,” 2019.
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[10] Y. Zhao, T. Li, X. Zhang, and C. Zhang, “Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future,” Renewable and Sustainable Energy Reviews, vol. 109, pp. 85–101, 2019.
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[13] J. Nordin et al., “Transient processing and analysis using AMPEL: alert management, photometry, and evaluation of light curves,” Astronomy & Astrophysics, vol. 631, p. A147, 2019.
[14] C. Chang, A. Drlica-Wagner, S. M. Kent, B. Nord, D. M. Wang, and M. H. Wang, “A machine learning approach to the detection of ghosting and scattered light artifacts in dark energy survey images,” Astronomy and Computing, vol. 36, p. 100474, 2021.
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[16] A. Serenelli et al., “Weighing stars from birth to death: mass determination methods across the HRD,” The Astronomy and Astrophysics Review, vol. 29, no. 1, p. 4, 2021.
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[18] S. K. Meher and G. Panda, “Deep learning in astronomy: a tutorial perspective,” The European Physical Journal Special Topics, vol. 230, no. 10, pp. 2285–2317, 2021.
[19] G. ElMasry and D.-W. Sun, “Principles of hyperspectral imaging technology,” in Hyperspectral imaging for food quality analysis and control, Elsevier, 2010, pp. 3–43.
[20] T. M. Brown, D. W. Latham, M. E. Everett, and G. A. Esquerdo, “Kepler input catalog: photometric calibration and stellar classification,” The Astronomical Journal, vol. 142, no. 4, p. 112, 2011.
[21] D. E. V. Berk et al., “Composite quasar spectra from the sloan digital sky survey,” The Astronomical Journal, vol. 122, no. 2, p. 549, 2001.
[22] A. P. Ruiz, M. Flynn, J. Large, M. Middlehurst, and A. Bagnall, “The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data Mining and Knowledge Discovery, vol. 35, no. 2, pp. 401–449, 2021.
[23] A. Udalski, M. Szymanski, I. Soszynski, and R. Poleski, “The optical gravitational lensing experiment. final reductions of the OGLE-III Data,” arXiv preprint arXiv:0807.3884, 2008.
[24] I. Jurkevich, “A method of computing periods of cyclic phenomena,” Astrophysics and Space Science, vol. 13, pp. 154–167, 1971.
[25] P. Mróz, “Identifying microlensing events using neural networks,” arXiv preprint arXiv:2008.11930, 2020.
[26] J. Hullman, S. Kapoor, P. Nanayakkara, A. Gelman, and A. Narayanan, “The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning,” presented at the Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, 2022, pp. 335–348.
[27] H. Schielzeth et al., “Robustness of linear mixed‐effects models to violations of distributional assumptions,” Methods in ecology and evolution, vol. 11, no. 9, pp. 1141–1152, 2020.
[28] M. A. Mansournia, M. Nazemipour, A. I. Naimi, G. S. Collins, and M. J. Campbell, “Reflection on modern methods: demystifying robust standard errors for epidemiologists,” International Journal of Epidemiology, vol. 50, no. 1, pp. 346–351, 2021.
[29] G. Nagaraj, J. C. Forbes, J. Leja, D. Foreman-Mackey, and C. C. Hayward, “A Bayesian Population Model for the Observed Dust Attenuation in Galaxies,” The Astrophysical Journal, vol. 932, no. 1, p. 54, 2022.
[30] Z. Sun, Y.-S. Ting, and Z. Cai, “Quasar Factor Analysis--An Unsupervised and Probabilistic Quasar Continuum Prediction Algorithm with Latent Factor Analysis,” arXiv preprint arXiv:2211.11784, 2022.
[31] J. A. Kader et al., “Blanco DECam Bulge Survey (BDBS). VII. Multiple Populations in Globular Clusters of the Galactic Bulge,” The Astrophysical Journal, vol. 940, no. 1, p. 76, 2022.
[32] W. G. Hartley et al., “The impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys,” Monthly Notices of the Royal Astronomical Society, vol. 496, no. 4, pp. 4769–4786, 2020.
[33] C. D. Huang et al., “Hubble space telescope observations of mira variables in the sn ia host ngc 1559: An alternative candle to measure the hubble constant,” The Astrophysical Journal, vol. 889, no. 1, p. 5, 2020.
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[35] G. Ashton et al., “Nested sampling for physical scientists,” Nature Reviews Methods Primers, vol. 2, no. 1, p. 39, 2022.
[36] J. Ching, S. Wu, and K.-K. Phoon, “Constructing quasi-site-specific multivariate probability distribution using hierarchical Bayesian model,” Journal of Engineering Mechanics, vol. 147, no. 10, p. 04021069, 2021.
[37] V. Belokurov, N. W. Evans, and Y. L. Du, “Light-curve classification in massive variability surveys—I. Microlensing,” Monthly Notices of the Royal Astronomical Society, vol. 341, no. 4, pp. 1373–1384, 2003.
[38] M. Sestovic, B.-O. Demory, and D. Queloz, “Investigating hot-Jupiter inflated radii with hierarchical Bayesian modelling,” Astronomy & Astrophysics, vol. 616, p. A76, 2018.
[39] M. Sunnåker, A. G. Busetto, E. Numminen, J. Corander, M. Foll, and C. Dessimoz, “Approximate bayesian computation,” PLoS computational biology, vol. 9, no. 1, p. e1002803, 2013.
[40] E. E. Ishida et al., “Cosmoabc: likelihood-free inference via population Monte Carlo approximate Bayesian computation,” Astronomy and Computing, vol. 13, pp. 1–11, 2015.
[41] L. Hong et al., “Bayesian modeling reveals metabolite‐dependent ultrasensitivity in the cyanobacterial circadian clock,” Molecular systems biology, vol. 16, no. 6, p. e9355, 2020.
[42] R. W. Wedderburn, “Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method,” Biometrika, vol. 61, no. 3, pp. 439–447, 1974.
[43] A. N. A. Tosteson and C. B. Begg, “A general regression methodology for ROC curve estimation,” Medical Decision Making, vol. 8, no. 3, pp. 204–215, 1988.
[44] E. D. Feigelson and G. J. Babu, Modern statistical methods for astronomy: with R applications. Cambridge University Press, 2012.
[45] J. C. Bezdek, FUZZY-MATHEMATICS IN PATTERN CLASSIFICATION. Cornell University, 1973.
[46] P. Domingos, “A few useful things to know about machine learning,” Communications of the ACM, vol. 55, no. 10, pp. 78–87, 2012.
[47] P. Huijse, P. A. Estevez, P. Protopapas, J. C. Principe, and P. Zegers, “Computational intelligence challenges and applications on large-scale astronomical time series databases,” IEEE Computational Intelligence Magazine, vol. 9, no. 3, pp. 27–39, 2014.
[48] A. Torralba, R. Fergus, and W. T. Freeman, “80 million tiny images: A large data set for nonparametric object and scene recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 30, no. 11, pp. 1958–1970, 2008.
[49] A. D’Isanto and K. L. Polsterer, “Photometric redshift estimation via deep learning-generalized and pre-classification-less, image based, fully probabilistic redshifts,” Astronomy & Astrophysics, vol. 609, p. A111, 2018.
[50] S. Zhu et al., “Intelligent Computing: The Latest Advances, Challenges, and Future,” Intelligent Computing, vol. 2, p. 0006, 2023.
[51] K. Boone, “ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning,” The Astronomical Journal, vol. 162, no. 6, p. 275, 2021.
[52] M. C. Robinson, R. C. Glen, and A. A. Lee, “Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction,” Journal of computer-aided molecular design, vol. 34, pp. 717–730, 2020.
[53] Z. Lin et al., “DeepSZ: identification of Sunyaev–Zel’dovich galaxy clusters using deep learning,” Monthly Notices of the Royal Astronomical Society, vol. 507, no. 3, pp. 4149–4164, 2021.
[54] J. Fan, R. Li, C.-H. Zhang, and H. Zou, Statistical foundations of data science. CRC press, 2020.
[55] M. M. Ahmed, A. Ganguly, A. Vashist, and S. M. P. Dinakarrao, “AWARe-Wi: A jamming-aware reconfigurable wireless interconnection using adversarial learning for multichip systems,” Sustainable Computing: Informatics and Systems, vol. 29, p. 100470, 2021.
[56] R. Sarmiento, J. H. Knapen, S. F. Sánchez, H. D. Sánchez, N. Drory, and J. Falcón-Barroso, “Capturing the physics of MaNGA galaxies with self-supervised Machine Learning,” The Astrophysical Journal, vol. 921, no. 2, p. 177, 2021.
[57] F. Offert and P. Bell, “Perceptual bias and technical metapictures: critical machine vision as a humanities challenge,” AI & SOCIETY, vol. 36, pp. 1133–1144, 2021.
[58] G. F. Striedter and R. G. Northcutt, Brains through time: a natural history of vertebrates. Oxford University Press, 2019.
Published
2023-03-21
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