Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916860328411136 |
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| author | Huang, Zhen Luo, Xin Zhang, Bin Feng, Jianchao Wu, Puxun Liu, Yu Liang, Nan |
| author_facet | Huang, Zhen Luo, Xin Zhang, Bin Feng, Jianchao Wu, Puxun Liu, Yu Liang, Nan |
| contents | In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) by Artificial Neural Networks (ANN) which is employed to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset are used to calibrate the Amati relation (\(E_{\rm p}\)-\(E_{\rm iso}\)) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high-redshift and the latest observational Hubble data (OHD) via a Markov Chain Monte Carlo numerical approach. For the Chevallier-Polarski-Linder (CPL) model within a flat universe, we obtain \(Ω_{\rm m} = 0.321^{+0.078}_{-0.069}\), \(h = 0.654^{+0.053}_{-0.071}\), \(w_0 = -1.02^{+0.67}_{-0.50}\), and \(w_a = -0.98^{+0.58}_{-0.58}\) at the 1-\(σ\) confidence level, which indicating a preference for dark energy with potential redshift evolution (\(w_a \neq 0\)). These findings by using ANN align closely with those derived from GRBs calibrated by using Gaussian Processes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Huang, Zhen Luo, Xin Zhang, Bin Feng, Jianchao Wu, Puxun Liu, Yu Liang, Nan Cosmology and Nongalactic Astrophysics In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) by Artificial Neural Networks (ANN) which is employed to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset are used to calibrate the Amati relation (\(E_{\rm p}\)-\(E_{\rm iso}\)) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high-redshift and the latest observational Hubble data (OHD) via a Markov Chain Monte Carlo numerical approach. For the Chevallier-Polarski-Linder (CPL) model within a flat universe, we obtain \(Ω_{\rm m} = 0.321^{+0.078}_{-0.069}\), \(h = 0.654^{+0.053}_{-0.071}\), \(w_0 = -1.02^{+0.67}_{-0.50}\), and \(w_a = -0.98^{+0.58}_{-0.58}\) at the 1-\(σ\) confidence level, which indicating a preference for dark energy with potential redshift evolution (\(w_a \neq 0\)). These findings by using ANN align closely with those derived from GRBs calibrated by using Gaussian Processes. |
| title | Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2506.08929 |