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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.12865 |
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| _version_ | 1866912647915503616 |
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| author | Jin, Zijian Rhee, Jaehyon |
| author_facet | Jin, Zijian Rhee, Jaehyon |
| contents | This paper builds upon ParamANN's novel approach (S. Pal & R. Saha 2024) of using ANNs to infer cosmological density parameters by determining optimal architecture for varying synthetic Hubble data SNRs in estimating the density parameters $Ω_{m, 0}$ and $Ω_{Λ, 0}$ across redshift values $z \in [0, 1]$. To generate the synthetic data, this study randomly sampled initial free parameter values at $z=0$ from theoretically motivated priors and evolved them backwards using the first Friedmann Equation to generate clean $H(z)$ curves. Then, this paper adds realistic noise of high, normal, and low SNR by sampling relative uncertainties from a Gaussian KDE on 47 real data observations compiled by A. Bouali et al. (2023). In the end, this study found that a RNN that uses BiLSTM is the most effective for high and normal SNR data across four quantitative metrics. On the other hand, a combination of convolution and recurrent layers that uses GRU performed the best for low SNR data across the same four metrics. A comparison between the results of this paper's ANN predictions and those of ParamANN shows that all architectures tested in this paper regardless of training SNR are statistically consistent within 1 standard deviation of ParamANN. However, most ANN results are not statistically consistent within 3 standard deviations of Planck Collaboration et al. (2020), showing a significant difference between ANN and the more traditional MCMC methods used by Planck collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Inference of Cosmological Density Parameters from Synthetic Hubble Expansion Data of Varying SNR Using Diverse Artificial Neural Network Architectures Jin, Zijian Rhee, Jaehyon Cosmology and Nongalactic Astrophysics This paper builds upon ParamANN's novel approach (S. Pal & R. Saha 2024) of using ANNs to infer cosmological density parameters by determining optimal architecture for varying synthetic Hubble data SNRs in estimating the density parameters $Ω_{m, 0}$ and $Ω_{Λ, 0}$ across redshift values $z \in [0, 1]$. To generate the synthetic data, this study randomly sampled initial free parameter values at $z=0$ from theoretically motivated priors and evolved them backwards using the first Friedmann Equation to generate clean $H(z)$ curves. Then, this paper adds realistic noise of high, normal, and low SNR by sampling relative uncertainties from a Gaussian KDE on 47 real data observations compiled by A. Bouali et al. (2023). In the end, this study found that a RNN that uses BiLSTM is the most effective for high and normal SNR data across four quantitative metrics. On the other hand, a combination of convolution and recurrent layers that uses GRU performed the best for low SNR data across the same four metrics. A comparison between the results of this paper's ANN predictions and those of ParamANN shows that all architectures tested in this paper regardless of training SNR are statistically consistent within 1 standard deviation of ParamANN. However, most ANN results are not statistically consistent within 3 standard deviations of Planck Collaboration et al. (2020), showing a significant difference between ANN and the more traditional MCMC methods used by Planck collaboration. |
| title | Probabilistic Inference of Cosmological Density Parameters from Synthetic Hubble Expansion Data of Varying SNR Using Diverse Artificial Neural Network Architectures |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2510.12865 |