Abstract :

The Discovery Of Exoplanets—planets Orbiting Stars Outside Our Solar System—has Significantly Advanced Our Understanding Of The Universe. Traditional Methods Of Detecting Exoplanets, Such As The Transit And Radial Velocity Techniques, Generate Massive Amounts Of Data That Are Challenging To Analyze Manually. In Recent Years, Artificial Intelligence (AI), Particularly Machine Learning Algorithms, Has Emerged As A Powerful Tool To Process This Data Efficiently And Accurately. AI Models Can Be Trained To Identify Subtle Patterns In Light Curves And Distinguish Genuine Planetary Signals From Noise Or False Positives. This Approach Accelerates The Discovery Process, Enhances The Accuracy Of Detections, And Enables The Identification Of Smaller, Earth-like Planets That May Have Otherwise Gone Unnoticed. The Integration Of AI In Exoplanet Research Represents A Transformative Step In Astronomy, Offering A Scalable And Intelligent Method To Explore The Vast Datasets Produced By Modern Space Telescopes Like Kepler And TESS. Key Words: Exoplanets, Artificial Intelligence (AI) , Machine Learning (ML), Deep Learning, Transit Method, Kepler Space Telescope, TESS (Transiting Exoplanet Survey Satellite), Light Curve Analysis, Neural Networks, Data Classification, Planet Detection.

Published:

15-7-2025

Issue:

Vol. 25 No. 7 (2025)

Page Nos:

404 - 413

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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