Reconstructing the Hubble diagram of gamma-ray bursts using deep learning

Abstract

We calibrate the distance and reconstruct the Hubble diagram of gamma-ray bursts (GRBs) using deep learning. We construct an artificial neural network, which combines the recurrent neural network and Bayesian neural network, and train the network using the Pantheon compilation of type-Ia supernovae. The trained network is used to calibrate the distance of 174 GRBs based on the Combo-relation. We verify that there is no evident redshift evolution of Combo-relation, and obtain the slope and intercept parameters, $\gamma =0.856_{-0.078}^{+0.083}$ and $\log A=49.661_{-0.217}^{+0.199}$, with an intrinsic scatter ${\sigma }_{\mathrm{int}}=0.228_{-0.040}^{+0.041}$. Our calibrating method is independent of cosmological model, thus the calibrated GRBs can be directly used to constrain cosmological parameters. It is shown that GRBs alone can tightly constrain the $\Lambda$CDM model, with ${\Omega }_{\mathrm{M}}=0.280_{-0.057}^{+0.049}$. However, the constraint on the $\omega$CDM model is relatively looser, with ${\Omega }_{\mathrm{M}}=0.345_{-0.060}^{+0.059}$ and $\omega <-1.414$. The combination of GRBs and Pantheon can tightly constrain the $\omega$CDM model, with ${\Omega }_{\mathrm{M}}=0.336_{-0.050}^{+0.055}$ and $\omega =-1.141_{-0.135}^{+0.156}$.

arXiv comment10 pages, 6 figures, 3 tables

References in corpus (42)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• emcee: The MCMC Hammer
• Planck 2013 results. XVI. Cosmological parameters
• Detection of the Baryon Acoustic Peak in the Large-Scale Correlation Function of SDSS Luminous Red Galaxies
• The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample
• The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
• A 2.4% Determination of the Local Value of the Hubble Constant
• Intrinsic spectra and energetics of BeppoSAX Gamma-Ray Bursts with known redshifts
• Tensions between the Early and the Late Universe
• The Physics of Gamma-Ray Bursts and Relativistic Jets
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
• GRB Formation Rates inferred from the Spectral Peak Energy - Luminosity Relation
• The collimation--corrected GRB energies correlate with the peak energy of their $\nu F_{\nu }$ spectrum
• Connection Between Energy-dependent Lags And Peak Luminosity In Gamma-ray Bursts
• New Parallaxes of Galactic Cepheids from Spatially Scanning the Hubble Space Telescope: Implications for the Hubble Constant
• A Photometric Redshift of z ~ 9.4 for GRB 090429B
• The Hubble Diagram to Redshift >6 from 69 Gamma-Ray Bursts
• Two accurate time-delay distances from strong lensing: Implications for cosmology
• Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples
• Constraining ${\Omega }_M$ and Dark Energy with Gamma-Ray Bursts
• Dark matter, dark energy, and alternate models: A review
• A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram
• The prompt-afterglow connection in Gamma-Ray Bursts: a comprehensive statistical analysis of Swift X-ray light-curves
• Systematic Uncertainties Associated with the Cosmological Analysis of the First Pan-STARRS1 Type Ia Supernova Sample
• Observational Constraints on Cosmological Models with the Updated Long Gamma-Ray Bursts
• A deep learning approach to cosmological dark energy models
• The updated luminosity correlations of gamma-ray bursts and cosmological implications
• Cosmological Models and Gamma-Ray Bursts Calibrated by Using Pade Method
• A new method optimized to use Gamma Ray Bursts as cosmic rulers
• New measurements of ${\Omega }_m$ from gamma-ray bursts
• Model independent calibrations of gamma ray bursts using machine learning
• Dropout as a Bayesian Approximation: Appendix
• Reconstructing the Cosmic Expansion History up to Redshift z=6.29 with the Calibrated Gamma-Ray Bursts
• A universal scaling for short and long gamma-ray bursts: E_{X,iso}-E_{gamma,iso}-E_{pk}
• Tracing dark energy history with gamma ray bursts
• Variation of the Amati Relation with the Cosmological Redshift: a Selection Effect or an Evolution Effect?
• Extending the Hubble diagram by gamma ray bursts
• Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the $\Lambda$CDM model
• Are long gamma-ray bursts standard candles?
• Model-independently calibrating the luminosity correlations of gamma-ray bursts using deep learning

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• Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample
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