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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
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Volume Published : 11
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| Paper Title: | Shinkai - A Physics-Informed Hybrid Machine Learning Pipeline for Celestial Object Classification |
| Authors Name: | Shubham Sanskar Routray , Shruti Patel , Omprakash Barapatre |
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IJRTI_207977
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| Published Paper Id: | IJRTI2511154 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | The rapid growth of astronomical surveys such as SDSS has created datasets too large and complex for manual interpretation, making automated celestial classification essential. Our research presents SHINKAI, a physics-informed hybrid machine learning pipeline designed to classify celestial objects with high scientific reliability. Unlike traditional models that rely solely on observational data, SHINKAI integrates astrophysical metrics derived from the Schwarzschild and Kerr metrics, the Friedmann equation, and gravitational wave strain to ensure that predictions remain physically meaningful. The system employs a stacking ensemble of Logistic Regression, Random Forest, and XGBoost, achieving a high classification accuracy of 95%, outperforming individual models in both precision and interpretability. In addition, a 3D spatial visualization module converts RA–Dec–Distance values into accurate Cartesian coordinates, enabling clear identification of object clusters in space. The results demonstrate that embedding astrophysical laws within machine learning significantly improves classification performance, reduces error, and provides deeper scientific insight. SHINKAI thus offers a robust, interpretable, and highly accurate approach for modern astronomical analysis. |
| Keywords: | Celestial Object Classification, Physics-Informed Models, Schwarzschild Metric, Kerr Metric, Friedmann Equation,Machine Learning, Ensemble Learning, Stacking Model, SDSS Dataset, Feature Engineering, 3D Spatial Visualization, Astronomy Data Analysis, Gravitational Wave Metrics. |
| Cite Article: | "Shinkai - A Physics-Informed Hybrid Machine Learning Pipeline for Celestial Object Classification", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b480-b485, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511154.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511154
Registration ID:207977
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: b480-b485 Country: Raipur, chhattisgarh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511154 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511154 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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