Authors - Randeep Singh Klair, Gurkunwar Singh, Ritik Verma, Satvik Rawal, Rajan Kakkar, Agamnoor Singh Vasir, Nilimp Rathore Abstract - The most accurate way to measure galaxy redshifts is using spectroscopy, but it takes a lot of computer power and telescope time. Despite their speed and scalability, photometric techniques are less precise. Thanks to large astronomical datasets, machine learning has become a potent technique for increasing cosmology research’s scalability and accuracy. On datasets such as the Sloan Digital Sky Survey, algorithms such as k-Nearest Neighbors, Random Forests, Support Vector Machines, Gradient Boosting, and Neural Networks are assessed using metrics like R-squared, Mean Absolute Error, and Root Mean Square Error. Ensemble approaches provide reliable accuracy, whereas neural networks are excellent at capturing non-linear correlations. Improvements in feature selection, hyperparameter tuning, and interpretability are essential to improving machine learning applications for photometric redshift estimation and providing deeper insights into cosmic structure and development.
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