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All Journal Tadris: Jurnal keguruan dan Ilmu Tarbiyah Agrifor : Jurnal Ilmu Pertanian dan Kehutanan Spektra: Jurnal Fisika dan Aplikasinya Jurnal Kajian Peradaban Islam Kappa Journal Indonesian Review of Physics (IRiP) Jurnal Simki Pedagogia Jurnal Kajian Peradaban Islam Bincang Sains dan Teknologi Research in Physics Education Time in Physics: Journal for Theoretical, Instrumentation, Material-Molecular, and Education Physics TarbiyahMU
Nurahman, Arip
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Transportation 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 Adi Jufriansah Adimayuda, Rizal Aldi Cahya Muhammad Ariefka, Reza Arif Jamali Arifah, Fina Nurul Ashel, Hazrati Bambang Supriyanto Budiana, Dodi Budiatin, Aniek Setiya Budiman Nasution Butch O. Saulon Cahya Prima, Eka E. Suhendi Eka Cahya Prima Fiqry, Rizalul Fitria Amalia, Irma Goldbert Harmuda Duva Sinaga Hadi Pranoto Herdianto, Nendar Ida Kaniawati Iman Nurzaman, Iman Ishafit Ishafit Ishafit Ishafit Kartika , Ika Khairil Anwar Lulut Alfaris Melati, Putri Sekar Nur Fadhilah, Dila Nyuswantoro, Ukta Indra Pribadi, Pandu Rahmawati, Mira Ramayana, Abdul Syamad Ramdani, Rida sari, Lasmita Saulon, Butch O. Septiyanto, Arifin Siagian , Ruben Cornelius Siagian, Ruben Cornelius Surya Gumilar, Surya Susylowati, Susylowati Walimudin, Ihsan Widodo Widodo Widodo Widodo winsyahputra Ritonga Yani, Sindi