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Model-independent calibration of gamma-ray bursts with Bayesian neural networks
Context. Gamma-ray bursts (GRBs) extend cosmological distance measurements beyond the range densely sampled by Type Ia supernovae, but their standardization requires reliable calibration and uncertainty estimates. Aims. We calibrate the Amati correlation without assuming a background cosmological
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