Dark Matter

Dark matter, comprising approximately 85% of the universe's matter, remains undetected, prompting research focused on inferring its properties indirectly through its gravitational effects on visible matter. Current research utilizes machine learning, particularly generative models like diffusion models and convolutional neural networks, to analyze data from simulations and observations (e.g., galaxy clusters, strong lensing, and time-series data from dark matter detection experiments), reconstructing dark matter density profiles and distributions. These advancements offer improved accuracy and efficiency in analyzing large datasets, enabling more precise constraints on dark matter models and potentially revealing its nature.