Exploring Celestial Object Characteristics: An In-depth Analysis of Quasars, Stars, and White Dwarfs Using the Sloan Digital Sky Survey (SDSS) Dataset
DOI:
https://doi.org/10.11594/timeinphys.2024.v2i1p1-16Keywords:
Sloan Digital Sky Survey (SDSS), Quasars, Statistical Analysis, Celestial Object Classification, Astronomical DatasetsAbstract
This research utilizes the Sloan Digital Sky Survey (SDSS) dataset, examining 12,884 observations to explore quasars, stars, and white dwarf objects. Magnitude data and coordinates across five filter bands are analyzed, revealing unique features through statistical methods. The identification of 77,429 quasars with 15 dimensions enhances the dataset. Thorough analyses of stellar and white dwarf classes, coupled with visualization techniques, unveil variable relationships. Residual validation and Gaussian kernel density plots confirm significant class differences. Non-linear regression and a normal distribution mixture model depict complex variable relationships. A parallel coordinates plot aids in interpreting data patterns, while predictive modeling via regression exposes meaningful coefficients. Logistic regression effectively classifies astronomical objects in the SDSS training data. This research contributes to understanding celestial object characteristics, offering valuable insights for astronomers and astrophysicists in analyzing large-scale astronomical datasets.
References
Faaique M. Overview of Big Data Analytics in Modern Astronomy. International Journal of Mathematics, Statistics, and Computer Science. 2024;2:96-113. https://doi.org/10.59543/ijmscs.v2i.8561
Schweizer L. Cosmic Odyssey: How Intrepid Astronomers at Palomar Observatory Changed Our View of the Universe. MIT Press; 2020. https://doi.org/10.7551/mitpress/11629.001.0001
Strittmatter PA, Neuhäuser R, Huber MC, et al. SAO/NASA ADS (null) Abstract Service. new Gresham A. SAO/NASA ADS (null) Abstract Service. MNRAS. 1945;105:80.
Farchi F, Farchi C, Touzi B, Mabrouki C. A comparative study on AI-based algorithms for cost prediction in pharmaceutical transport logistics. Acadlore Trans Mach Learn. 2023;2(3):129-141. https://doi.org/10.56578/ataiml020302
Khanam A. Visualizing Linguistics: Harnessing Canva's Power For Collaborative Groups. Boletin de Literatura Oral-The Literary Journal. 2023;10(1):1811-1825.
Pinter C. Mind and the Cosmic Order: How the Mind Creates the Features & Structure of All Things, and Why This Insight Transforms Physics. Springer; 2020. https://doi.org/10.1007/978-3-030-50083-2
Kaklauskas A, Abraham A, Ubarte I, et al. A review of AI cloud and edge sensors, methods, and applications for the recognition of emotional, affective and physiological states. Sensors. 2022;22(20):7824. https://doi.org/10.3390/s22207824
Huang TK. Exploiting Non-sequence Data in Dynamic Model Learning. Published online 2013.
Gardner K. Analyzing The Uncertainty of Sloan Digital Sky Survey Data. Published online 2020.
Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. https://doi.org/10.7717/peerj-cs.623
Glikman E, Lacy M, LaMassa S, et al. The WISE-2MASS Survey: Red Quasars Into the Radio Quiet Regime. The Astrophysical Journal. 2022;934(2):119. https://doi.org/10.3847/1538-4357/ac6bee
de Melo MB, Daldegan‐Bueno D, Menezes Oliveira MG, de Souza AL. Beyond ANOVA and MANOVA for repeated measures: Advantages of generalized estimated equations and generalized linear mixed models and its use in neuroscience research. European Journal of Neuroscience. 2022;56(12):6089-6098. https://doi.org/10.1111/ejn.15858
Rodu J, Kafadar K. The q-q Boxplot. Journal of Computational and Graphical Statistics. 2022;31(1):26-39. https://doi.org/10.1080/10618600.2021.1938586
Santander P, Quast A, Olbrisch C, et al. Comprehensive 3D analysis of condylar morphology in adults with different skeletal patterns-a cross-sectional study. Head & Face Medicine. 2020;16(1):1-10. https://doi.org/10.1186/s13005-020-00245-z
Wu P, Ferrari RM, Liu Y, Van Wingerden JW. Data-driven incipient fault detection via canonical variate dissimilarity and mixed kernel principal component analysis. IEEE Transactions on Industrial Informatics. 2020;17(8):5380-5390. https://doi.org/10.1109/TII.2020.3029900
Farmer J, Allen E, Jacobs DJ. Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities. Mathematics. 2022;11(1):155. https://doi.org/10.3390/math11010155
Ding C, Cao X, Yu B, Ju Y. Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity. Transportation Research Part A: Policy and Practice. 2021;148:22-35. https://doi.org/10.1016/j.tra.2021.03.021
van de Schoot R, Depaoli S, King R, et al. Bayesian statistics and modelling. Nature Reviews Methods Primers. 2021;1(1):1. https://doi.org/10.1038/s43586-020-00001-2
Vuong QH, La VP, Nguyen MH, Ho MT, Tran T, Ho MT. Bayesian analysis for social data: A step-by-step protocol and interpretation. MethodsX. 2020;7:100924. https://doi.org/10.1016/j.mex.2020.100924
Chaleshtori AE, Aghaie A. A novel bearing fault diagnosis approach using the Gaussian mixture model and the weighted principal component analysis. Reliability Engineering & System Safety. 2024;242:109720. https://doi.org/10.1016/j.ress.2023.109720
Blumenschein M, Zhang X, Pomerenke D, Keim DA, Fuchs J. Evaluating reordering strategies for cluster identification in parallel coordinates. In: Vol 39. Wiley Online Library; 2020:537-549. https://doi.org/10.1111/cgf.14000
Antor AF. Application of Machine Learning Models for Half-Hour Ahead Solar Irradiance and Wind Speed Prediction. Colorado State University; 2021.
Idriss LK, Owais M. Global sensitivity analysis for seismic performance of shear wall with high-strength steel bars and recycled aggregate concrete. Construction and Building Materials. 2024;411:134498. https://doi.org/10.1016/j.conbuildmat.2023.134498
Wang W, Liu W. PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery. Briefings in Bioinformatics. 2021;22(6):bbab212. https://doi.org/10.1093/bib/bbab212
Eyduran E, Yakubu A, Duman H, Aliyev P, Tırınk C. Predictive modeling of multivariate adaptive regression splines: An R Tutorial. Veri Madenciliği Yöntemleri: Tarim Alanında Uygulamaları 1th ed Çanakkale, Türkiye. Published online 2020:25-48.
Karataş Z, Tagay Ö. The relationships between resilience of the adults affected by the covid pandemic in Turkey and Covid-19 fear, meaning in life, life satisfaction, intolerance of uncertainty and hope. Personality and Individual Differences. 2021;172:110592. https://doi.org/10.1016/j.paid.2020.110592
Fathipour-Azar H. New interpretable shear strength criterion for rock joints. Acta Geotechnica. 2022;17(4):1327-1341. https://doi.org/10.1007/s11440-021-01442-z
Hassija V, Chamola V, Mahapatra A, et al. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation. Published online 2023:1-30. https://doi.org/10.1007/s12559-023-10179-8
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Copyright (c) 2024 Ruben Cornelius Siagian, Arip Nurahman, Goldberd Harmuda Duva Sinaga, Reza Ariefka, Pandu Pribadi
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