arXiv:2606.00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks. In response, machine learning (ML), and emulation in particular, has emerged as a powerful approach to accelerate and enhance inference pipelines. In this work, we present 21cmEMUv3, an emulator trained on 21cmFASTv3 simulations that model both atomically and molecularly cooling galaxies. 21cmEMUv3 is conditioned on $\sigma_8$ and ten astrophysical parameters t
The increasing availability of cosmic dawn and epoch of reionization observations is driving the need for rapid data interpretation, making ML-based emulation crucial.
This development allows for faster and more robust theoretical interpretation of complex astrophysical phenomena, accelerating scientific discovery and understanding of the early universe.
The ability to quickly emulate astrophysical simulations significantly reduces the computational bottleneck for cosmological research, enabling more extensive parameter space exploration.
- · Astrophysicists
- · Cosmology research
- · Machine learning in science
- · High-performance computing
- · Traditional simulation methods (time-wise)
Scientific research into the early universe accelerates through improved data analysis capabilities.
New theoretical models and insights into cosmic dawn and reionization lead to refined cosmological parameters.
The application of advanced ML techniques to other complex scientific domains becomes more widespread, setting a precedent.
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