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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2508.05842 |
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| _version_ | 1866912526952824832 |
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| author | Hosseini, S. Mobina Salmasi, Bahareh Soleimanpour |
| author_facet | Hosseini, S. Mobina Salmasi, Bahareh Soleimanpour |
| contents | The X-ray photons substantially affect the thermal and ionization states of the intergalactic medium (IGM) during the Epoch of Reionization (EoR), thereby significantly influencing the 21-cm line observables such as its sky-averaged (global) brightness temperature. Nevertheless, the complicated dependency of astrophysical processes on a broad spectrum of parameters, including X-ray efficiency, spectral characteristics, and gas dynamics, makes precisely simulating the effect of X-ray flux challenging. Traditional approaches, including N-body and hydrodynamical simulations, are computationally intensive and struggle to explore high-dimensional parameter spaces efficiently. We present a stacked hybrid model trained on a specific simulation intended to reconstruct the effect of X-ray flux on the global 21-cm brightness temperature during the EoR. Along with Convolutional Neural Networks (CNNs), this architecture combines two substantial forms of recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), therefore enabling fast adaptation to several X-ray flux levels. Without demanding repeated simulations, this emulator preserves temporal and spatial dependencies and generalizes to unseen parameter combinations. This matter reduces computation time by a factor of one million while preserving excellent prediction accuracy of 99.93\%, facilitating studies on high-dimensional parameter inference and sensitivity with an error margin of less than 0.35 mK. Our LSTM-GRU-CNN emulator combines recurrent and convolutional architectures to enable a robust and scalable analysis of X-ray heating effects on the global 21-cm brightness temperature during the EoR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature Hosseini, S. Mobina Salmasi, Bahareh Soleimanpour Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics The X-ray photons substantially affect the thermal and ionization states of the intergalactic medium (IGM) during the Epoch of Reionization (EoR), thereby significantly influencing the 21-cm line observables such as its sky-averaged (global) brightness temperature. Nevertheless, the complicated dependency of astrophysical processes on a broad spectrum of parameters, including X-ray efficiency, spectral characteristics, and gas dynamics, makes precisely simulating the effect of X-ray flux challenging. Traditional approaches, including N-body and hydrodynamical simulations, are computationally intensive and struggle to explore high-dimensional parameter spaces efficiently. We present a stacked hybrid model trained on a specific simulation intended to reconstruct the effect of X-ray flux on the global 21-cm brightness temperature during the EoR. Along with Convolutional Neural Networks (CNNs), this architecture combines two substantial forms of recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), therefore enabling fast adaptation to several X-ray flux levels. Without demanding repeated simulations, this emulator preserves temporal and spatial dependencies and generalizes to unseen parameter combinations. This matter reduces computation time by a factor of one million while preserving excellent prediction accuracy of 99.93\%, facilitating studies on high-dimensional parameter inference and sensitivity with an error margin of less than 0.35 mK. Our LSTM-GRU-CNN emulator combines recurrent and convolutional architectures to enable a robust and scalable analysis of X-ray heating effects on the global 21-cm brightness temperature during the EoR. |
| title | Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2508.05842 |