A deep learning algorithm for black hole spin estimation using hot-spot secondary images
A deep learning algorithm named STIHOS uses secondary images of hot spots to accurately estimate black hole spin and inclination by analyzing the angle difference between primary and secondary images ($\Delta P_A$). The system models hot spot simulations using general relativity and trains a Multi-Layer Perceptron (MLP) to map observed angles to physical parameters, providing robust results even under observational noise.