LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Yeo-Johnson preprocessing combined with moderate amplitude compression provides the best trade-off for conditional diffusion models emulating 3D 21 cm lightcones, supported by MAE_std on the global signal, though biases remain in power spectra and higher-order statistics.
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Three-dimensional Conditional Diffusion Models for Cosmological 21 cm Lightcone Emulation
Yeo-Johnson preprocessing combined with moderate amplitude compression provides the best trade-off for conditional diffusion models emulating 3D 21 cm lightcones, supported by MAE_std on the global signal, though biases remain in power spectra and higher-order statistics.
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Application of Machine Learning to 21 cm Cosmology
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.