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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Reviews how cross-correlating SKAO 21-cm LIM with other lines like [CII], CO, and Ly-alpha can mitigate systematics, enhance sensitivity, and disentangle cosmological from astrophysical parameters.
Reviews multiple higher-order statistics for 21-cm intensity mapping and forecasts their detectability with SKAO, incorporating noise and foreground effects.
A review chapter summarizing theoretical 21-cm signatures from Cosmic Dawn and Reionization and their detectability with SKA-Low.
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.
A review chapter on tools for inferring galaxy and IGM properties from the 21 cm signal using the initial SKA-Low array configuration.
An overview summarizing SKA-Low 21cm experiments for power spectrum, tomography, 21-cm forest, and cross-correlations, plus critical telescope features, building on the 2015 SKA Science Book.
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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.