International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | EXOENSEMBLE: An Approach to Exoplanet Spectrum Extraction |
|---|---|
| ABSTRACT | Exoplanetary atmospheric characterization is a critical challenge in modern astronomy due to the extremely weak signals and complex noise present in observational data. Traditional analytical methods often fail to accurately extract spectral features under such conditions. This paper introduces EXOENSEMBLE, a hybrid ensemble-based framework that integrates advanced signal processing, Shared-Weight 1D Convolutional Neural Networks (CNN), and Rational Quadratic Neural Networks (RQ-NN) for robust spectrum reconstruction. The system performs multi-instrument data fusion and applies rigorous calibration techniques to recover faint atmospheric signatures. By leveraging deep ensemble learning with Gaussian Log-Likelihood constraints, EXOENSEMBLE produces both mean spectra and uncertainty estimates, enabling reliable detection of atmospheric compounds such as water vapor, methane, and carbon dioxide. Experimental insights demonstrate improved denoising, generalization, and uncertainty quantification compared to traditional methods, making the framework highly suitable for next-generation exoplanet missions. |
| AUTHOR | KOLLU POOJITHA, MOHAMMED ABDUL KALAM KHAN, N.MUSRAT SULTANA, DR.K.RAJITHA, DR.V. SUBBARAMAIAH Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Gandipet, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404103 |
| pdf/103_EXOENSEMBLE An Approach to Exoplanet Spectrum Extraction.pdf | |
| KEYWORDS | |
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