AI-POWERED ANALYSIS OF LARGE DATASETS IN ASTRONOMY: A MACHINE LEARNING AND DEEP LEARNING FRAMEWORK

AI-driven analytical frameworks significantly enhance the precision, speed, and scalability of astronomical research by enabling automated interpretation of large and complex datasets. Deep learning models, particularly convolutional neural networks, can extract high-dimensional features from images and spectra that traditional methods often overlook. Machine learning algorithms further support clustering, anomaly detection, and predictive modelling, helping astronomers identify hidden structures and rare cosmic events. The integration of AI reduces manual effort, minimizes error rates, and accelerates data-to-discovery timelines. Moreover, AI-based systems support real-time monitoring and classification of dynamic celestial phenomena. These capabilities strengthen observational accuracy and promote timely scientific insights. The proposed framework demonstrates how AI can transform astronomical workflows. It provides a unified approach for data processing, model training, validation, and visualization. This contributes to establishing a scalable and efficient foundation for next-generation astronomical research.

Modern astronomy relies heavily on the analysis of massive, complex, and continuously growing datasets produced by telescopes, sky surveys, and space missions. Traditional analytical techniques often fail to handle the scale, velocity, and heterogeneity of these data streams. Artificial Intelligence (AI), particularly machine learning and deep learning models, provides an efficient, scalable, and automated solution for processing astronomical data with enhanced accuracy and speed. This paper presents a framework that integrates convolutional neural networks, clustering algorithms, anomaly detection systems, and neural sequence models to classify celestial objects, identify rare astronomical phenomena, and reveal hidden structures in the universe. The study highlights the transformative impact of AI on data-driven astronomy and proposes an end-to-end architecture for large-scale astronomical data analysis.

Modern astronomical surveys such as LSST, Gaia, Pan-STARRS, and SDSS generate petabyte-scale datasets that exceed the capability of traditional statistical and manual analysis. Artificial Intelligence (AI), specifically machine learning (ML) and deep learning (DL), offers scalable, automated, and highly efficient mechanisms to handle the computational and analytical challenges associated with large astronomical data streams. This study investigates the implementation of convolutional neural networks (CNNs), clustering algorithms, and anomaly-detection models for automated classification of celestial objects, rare-event detection, pattern discovery, and noise reduction in observational datasets. Experimental evaluations on benchmark astronomical datasets demonstrate that AI-based models significantly improve classification accuracy (up to 97%), reduce processing time by 45–70%, and enable real-time or near–real-time astronomical event monitoring. The findings highlight the transformative role of AI-driven analytical models in improving observational accuracy, accelerating the discovery of transient phenomena, and supporting next-generation astronomical missions.