New AI Algorithm Enhances Dark Matter Detection

David Harvey's deep-learning model distinguishes dark matter interactions from cosmic noise with high accuracy.

Algorithm developed by David Harvey at EPFL uses deep learning to analyze galaxy cluster images.
The model, named 'Inception,' achieves 80% accuracy in identifying dark matter self-interactions.
Inception distinguishes between dark matter effects and active galactic nuclei feedback.
The AI maintains its performance even with observational noise, simulating real telescope data.
This method promises to accelerate dark matter research using data from future telescopes like Euclid.