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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Domain adaptation with an ensemble of CNN and transformer models trained on DES detects 20,180 LSBGs and 434 UDGs in KiDS DR5, with structural parameters and environmental trends consistent with known samples.
ComPACT is a new SZ-selected galaxy cluster catalogue from CNN analysis of ACT+Planck data with 2,962 candidates, ~60% confirmation, 116 new redshifts, 158 new masses, and five new massive clusters at z>0.7 that increase the known high-mass high-z population by ~10%.
Review chapter organizing machine learning methods for 21 cm cosmology into observation, theory, and inference domains.
citing papers explorer
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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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From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies
Domain adaptation with an ensemble of CNN and transformer models trained on DES detects 20,180 LSBGs and 434 UDGs in KiDS DR5, with structural parameters and environmental trends consistent with known samples.
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ComPACT: Mass-Redshift Properties of the galaxy cluster catalogue
ComPACT is a new SZ-selected galaxy cluster catalogue from CNN analysis of ACT+Planck data with 2,962 candidates, ~60% confirmation, 116 new redshifts, 158 new masses, and five new massive clusters at z>0.7 that increase the known high-mass high-z population by ~10%.
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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.