LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
Inferring astrophysics and dark matter properties from 21 cm tomography using deep learning
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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.
citing papers explorer
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition generative networks for LHC events, yielding faster convergence, higher quality, and generalization to unseen processes.
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Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low
A review chapter on tools for inferring galaxy and IGM properties from the 21 cm signal using the initial SKA-Low array configuration.
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Overview of 21cm Experiments at high redshift with SKAO
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.
- Application of Machine Learning to 21 cm Cosmology