Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916625114988544 |
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| author | Zhang, Bin Wang, Huifeng Nong, Xiaodong Wang, Guangzhen Wu, Puxun Liang, Nan |
| author_facet | Zhang, Bin Wang, Huifeng Nong, Xiaodong Wang, Guangzhen Wu, Puxun Liang, Nan |
| contents | In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation (\unboldmath{$E_{\rm p}$-$E_{\rm iso}$}) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09440 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning Zhang, Bin Wang, Huifeng Nong, Xiaodong Wang, Guangzhen Wu, Puxun Liang, Nan Cosmology and Nongalactic Astrophysics In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) with the machine learning (ML) algorithms from the Pantheon+ sample of type Ia supernovae in a cosmology-independent way. By using K-Nearest Neighbors (KNN) and Random Forest (RF) selected with the best performance in the ML algorithms, we calibrate the Amati relation (\unboldmath{$E_{\rm p}$-$E_{\rm iso}$}) relation with the A219 sample to construct the Hubble diagram of GRBs. Via the Markov Chain Monte Carlo numerical method with GRBs at high redshift and latest observational Hubble data, we find the results of constraints on cosmological models by using KNN and RF algorithms are consistent with those obtained from GRBs calibrated by using the Gaussian Process. |
| title | Model-independent Gamma-Ray Bursts Constraints on Cosmological Models Using Machine Learning |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2312.09440 |