Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
A time - luminosity correlation for Gamma Ray Bursts in the X - rays
3 Pith papers cite this work. Polarity classification is still indexing.
abstract
Gamma ray bursts (GRBs) have recently attracted much attention as a possible way to extend the Hubble diagram to very high redshift. However, the large scatter in their intrinsic properties prevents directly using them as distance indicator so that the hunt is open for a relation involving an observable property to standardize GRBs in the same way as the Phillips law makes it possible to use Type Ia Supernovae (SNeIa) as standardizable candles. We use here the data on the X - ray decay curve and spectral index of a sample of GRBs observed with the Swift satellite. These data are used as input to a Bayesian statistical analysis looking for a correlation between the X - ray luminosity L_X(T_a) and the time constant T_a of the afterglow curve. We find a linear relation between \log{[L_X(T_a)]} and \log{[T_a/(1+z)]} with an intrinsic scatter sigma_{int} = 0.33 comparable to previously reported relations. Remarkably, both the slope and the intrinsic scatter are almost independent on the matter density Omega_M and the constant equation of state w of the dark energy component thus suggesting that the circularity problem is alleviated for the $L_X - T_a$ relation.
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Model-independent calibration of Gamma-Ray Bursts with neural networks
Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Cosmographic Taylor and Padé models fitted to Pantheon+SH0ES+GRB+DESI BAO data yield redshift drift predictions compatible with ΛCDM and ω0ω1CDM at 1-2σ, with mock drift data tightening q0 and j0 bounds.
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