Artificial Intelligence and Statistical Methods in Modern Astrophysics: From Exoplanet Detection to Black Hole Characterization
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Abstract
Artificial intelligence (AI) and advanced statistical methods are rapidly transforming astrophysics, enabling efficient analysis of increasingly complex and voluminous datasets. These methods have applications across multiple domains, including exoplanet detection, time-domain astronomy, stellar variability, pulsar studies, and black hole and galaxy evolution modelling. This review synthesizes recent developments in AI and data-driven approaches across these subfields. In exoplanet science, machine learning algorithms enhance detection and classification, while in time-domain astronomy, robotic telescopes and automated pipelines improve transient discovery. AI also facilitates modelling of black hole growth, binary mergers, and galaxy co-evolution. Additionally, statistical methods applied to pulsars, cosmic ray variability, and maser studies provide new insights into stellar populations and Galactic structure. Challenges such as interpretability, reproducibility, and biases are discussed, along with prospects for integrating AI into multi-messenger and multi-wavelength astrophysics. This review demonstrates the central role of AI and statistics in accelerating discovery across cosmic scales.
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References
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