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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences INDONESIAN JOURNAL OF APPLIED PHYSICS Al-MARSHAD: Jurnal Astronomi Islam dan Ilmu-Ilmu Berkaitan Journal of Environmental Science and Sustainable Development Didaktika: Jurnal Kependidikan ALACRITY : Journal Of Education Elementary School: Jurnal Pendidikan dan Pembelajaran ke-SD-an Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics EDUCARE: Journal of Primary Education
Ariefka, Reza
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Humanities Astronomy Earth & Planetary Sciences Education Energy Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Exploring Celestial Object Characteristics: An In-depth Analysis of Quasars, Stars, and White Dwarfs Using the Sloan Digital Sky Survey (SDSS) Dataset Siagian, Ruben Cornelius; Nurahman, Arip; Sinaga, Goldberd Harmuda Duva; Ariefka, Reza; Pribadi, Pandu
TIME in Physics Vol. 2 No. 1 (2024): March
Publisher : Universitas Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/timeinphys.2024.v2i1p1-16

Abstract

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.
Co-Authors Aditya, Reviyanti Agatha Saputri Aldi Cahya Muhammad Amiruddin Takda Anggreni, Monica Ariyanti, Etika Aryani, Devi Aunzo, Jr., Rodulfo T. Eka Aryani, Eka Fauziah, Rosynanda Nur Firdaus, Anas Noor Goldbert Harmuda Duva Sinaga Hassan, Rohana Karim, Mohammad Alfin Khafidotul Khoriyah Lulut Alfaris Mardiana Napirah Muhammad Anas Nurahman, Arip Nyuswantoro, Ukta Indra Prasetya Suseno, Bima Pribadi, Pandu Rahdiana, Nana Rosely, Annisa Sahroni, Taufik Roni, Mr. Sari, Sasmita Sasmita Sari Siagian, Ruben Cornelius Suhara, Ade Suritno Fayanto Vivi Hastuti Rufa Mongkito Yasin, Verdi