Dark Matter Mapping with Supercomputers
Researchers are using supercomputers and artificial intelligence (AI) to map dark matter, a largely invisible substance thought to make up most of the universe's mass. By leveraging powerful computing tools, scientists are developing complex simulations that reveal how dark matter interacts within galaxies and influences cosmic structures.
AI and Supercomputers in Dark Matter Research
A notable example is Argonne’s Aurora supercomputer, which scales machine learning models to analyze vast datasets of cosmic phenomena, advancing our understanding of dark matter and potentially redefining current physics models. Aurora's application allows researchers to run simulations previously beyond reach, improving accuracy and efficiency by combining physics-informed AI with traditional cosmological models.
Projects like those at ETH Zurich take this further by using deep learning on simulated data to uncover dark matter’s distribution across the universe. These models are trained on artificial data first, which helps to “teach” the AI about possible patterns within dark matter, refining it with real telescope data afterward. This approach enables unprecedented accuracy, enhancing our understanding of dark matter's impact on the universe’s structure while setting the stage for future applications of AI in astronomy.
Exploring the “Dark Universe”
Such breakthroughs are foundational for exploring the “dark universe” since dark matter, unlike visible matter, doesn’t emit light or energy, making it observable only through indirect means like gravitational effects on visible objects.
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