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.
Determination of the intrinsic Luminosity Time Correlation in the X-ray Afterglows of GRBs
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abstract
Gamma-ray bursts (GRBs), which have been observed up to redshifts z approx 9.5 can be good probes of the early universe and have the potential of testing cosmological models. The analysis by Dainotti of GRB Swift afterglow lightcurves with known redshifts and definite X-ray plateau shows an anti-correlation between the rest frame time when the plateau ends (the plateau end time) and the calculated luminosity at that time (or approximately an anti-correlation between plateau duration and luminosity). We present here an update of this correlation with a larger data sample of 101 GRBs with good lightcurves. Since some of this correlation could result from the redshift dependences of these intrinsic parameters, namely their cosmological evolution we use the Efron-Petrosian method to reveal the intrinsic nature of this correlation. We find that a substantial part of the correlation is intrinsic and describe how we recover it and how this can be used to constrain physical models of the plateau emission, whose origin is still unknown. The present result could help clarifing the debated issue about the nature of the plateau emission.
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2024 1verdicts
UNVERDICTED 1representative citing papers
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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.