Nilanjana Bagchi Aurpa

arXiv2026

Revisiting 2D and 3D Dainotti Correlations for GRBs Using Bayesian Neural Networks

Gamma-ray bursts (GRBs) are promising cosmological probes, but their use as standard candles is limited by the circularity problem, necessitating model-independent calibration of GRB luminosity correlations. We revisit the two-dimensional (2D) and three-dimensional (3D) Dainotti correlations using Bayesian Neural Networks (BNNs) trained on the updated Observational Hubble Data (OHD) and Pantheon+ Type Ia Supernova sample. The reconstructed luminosity distances are used to calibrate the Platinum and Narendra et al. GRB samples. We constrain the parameters of the 2D Dainotti relation and the 3D fundamental plane, and examine the impact of calibration datasets and GRB sample selection. Calibration achieved using Pantheon+ yields tighter constraints than OHD, while the 3D correlation exhibits lower intrinsic scatter than the 2D relation. Our results demonstrate that BNNs provide a robust framework for model independent calibration of GRB luminosity correlations with reliable uncertainty propagation. Further, the underlying distance probe is a key factor in model-independent calibration, determining both the size of the GRB samples and the precision of the resulting constraints.

astro-ph.CO↗

arXiv2026

Reconstructing Gamma Ray Burst Energy Relations with Observational H(z) data in Neural Network Framework

Gamma-ray bursts (GRBs) offer a powerful probe of the cosmic expansion history far beyond the redshift range accessible to Type Ia supernovae. However, the study of cosmological models using GRBs is hindered by the circularity problem, which arises from assuming a fiducial cosmological model during GRB luminosity distance calibration. In this work, we perform a model-independent calibration of GRB luminosity relations using observational measurements of the Hubble parameter from the A220 and J220 compilations, thereby avoiding explicit cosmological assumptions. We employ an Artificial Neural Network to reconstruct the calibration relation directly from the data. In addition, we implement a Bayesian Neural Network framework as an alternative approach, enabling a data-driven treatment of both statistical and systematic uncertainties. The calibrated GRB sample is used to constrain the Amati relation, and we systematically compare the outcomes obtained from different calibration techniques and datasets. We find that the Amati relation slopes derived from the two neural network approaches are consistent with each other and with previous low-redshift calibrations obtained using model-independent methods. The Bayesian Neural Network approach provides a more robust framework for propagating uncertainties in the calibration procedure.

astro-ph.CO↗