Ponente
Descripción
We apply two variants of Physics-Informed Neural Networks (PINNs) to reconstruct the Type Ia supernova absolute magnitude M_B(z) from joint BAO and supernova data under four cosmological models (\LambdaCDM, CPL, GEDE, \Lambda_sCDM) and two DESI DR2 fiducial sets. A heteroscedastic single-network method tested across four constraint configurations establishes that the Etherington distance duality relation is more fundamental constraint than cosmological model priors, with DDR violations of 30--52 mmag under physical constraints versus 85--2330 mmag without. Under full constraints all models recover M_B \approx -19.3 mag with biases below 0.05 mag. A Fisher information-weighted two-network variant trains independent networks on BAO and SN data, providing clean probe separation and finding no significant M_B evolution in z \in [0.3, 1.5]. The heteroscedastic method identifies a persistent 2-3\sigma residual at z \sim 0.4-0.5 that is consistent across all four models and both fiducials; the Fisher method finds no significant pointwise deviation in z\in[0.3,1.5] but shows a systematic separation of redshift-binned M_B distributions consistent with the same underlying tension. While the origin of this feature remains ambiguous, its model-independence and cross-method consistency warrant further investigation with forthcoming data.
