Model-independent calibration of Gamma-Ray Bursts with neural networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911655585120256 |
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| author | Mukherjee, Purba Dainotti, Maria Giovanna Dialektopoulos, Konstantinos F. Said, Jackson Levi Mifsud, Jurgen |
| author_facet | Mukherjee, Purba Dainotti, Maria Giovanna Dialektopoulos, Konstantinos F. Said, Jackson Levi Mifsud, Jurgen |
| contents | The $Λ$ Cold Dark Matter ($Λ$CDM) cosmological model has been highly successful in predicting cosmic structure and evolution, yet recent precision measurements have highlighted discrepancies, especially in the Hubble constant inferred from local and early-Universe data. Gamma-ray bursts (GRBs) present a promising alternative for cosmological measurements, capable of reaching higher redshifts than traditional distance indicators. This work leverages GRBs to refine cosmological parameters independently of the $Λ$CDM framework. Using the Platinum compilation of long GRBs, we calibrate the Dainotti relations-empirical correlations among GRB luminosity properties-as standard candles through artificial neural networks (ANNs). We analyze both the 2D and 3D Dainotti calibration relations, leveraging an ANN-driven Markov Chain Monte Carlo approach to minimize scatter in the calibration parameters, thereby achieving a stable Hubble diagram. This ANN-based calibration approach offers advantages over Gaussian processes, avoiding issues such as kernel function dependence and overfitting. Our results emphasize the need for model-independent calibration approaches to address systematic challenges in GRB luminosity variability, ultimately extending the cosmic distance ladder in a robust way. By addressing redshift evolution and reducing systematic uncertainties, GRBs can serve as reliable high-redshift distance indicators, offering critical insights into current cosmological tensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-independent calibration of Gamma-Ray Bursts with neural networks Mukherjee, Purba Dainotti, Maria Giovanna Dialektopoulos, Konstantinos F. Said, Jackson Levi Mifsud, Jurgen Cosmology and Nongalactic Astrophysics The $Λ$ Cold Dark Matter ($Λ$CDM) cosmological model has been highly successful in predicting cosmic structure and evolution, yet recent precision measurements have highlighted discrepancies, especially in the Hubble constant inferred from local and early-Universe data. Gamma-ray bursts (GRBs) present a promising alternative for cosmological measurements, capable of reaching higher redshifts than traditional distance indicators. This work leverages GRBs to refine cosmological parameters independently of the $Λ$CDM framework. Using the Platinum compilation of long GRBs, we calibrate the Dainotti relations-empirical correlations among GRB luminosity properties-as standard candles through artificial neural networks (ANNs). We analyze both the 2D and 3D Dainotti calibration relations, leveraging an ANN-driven Markov Chain Monte Carlo approach to minimize scatter in the calibration parameters, thereby achieving a stable Hubble diagram. This ANN-based calibration approach offers advantages over Gaussian processes, avoiding issues such as kernel function dependence and overfitting. Our results emphasize the need for model-independent calibration approaches to address systematic challenges in GRB luminosity variability, ultimately extending the cosmic distance ladder in a robust way. By addressing redshift evolution and reducing systematic uncertainties, GRBs can serve as reliable high-redshift distance indicators, offering critical insights into current cosmological tensions. |
| title | Model-independent calibration of Gamma-Ray Bursts with neural networks |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2411.03773 |