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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2404.05794 |
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| _version_ | 1866914744926994432 |
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| author | Nasir, Fahad Gaikwad, Prakash Davies, Frederick B. Bolton, James S. Puchwein, Ewald Bosman, Sarah E. I. |
| author_facet | Nasir, Fahad Gaikwad, Prakash Davies, Frederick B. Bolton, James S. Puchwein, Ewald Bosman, Sarah E. I. |
| contents | Unveiling the thermal history of the intergalactic medium (IGM) at $4 \leq z \leq 5$ holds the potential to reveal early onset HeII reionization or lingering thermal fluctuations from HI reionization. We set out to reconstruct the IGM gas properties along simulated Lyman-alpha forest data on pixel-by-pixel basis, employing deep Bayesian neural networks. Our approach leverages the Sherwood-Relics simulation suite, consisting of diverse thermal histories, to generate mock spectra. Our convolutional and residual networks with likelihood metric predicts the Ly$α$ optical depth-weighted density or temperature for each pixel in the Ly$α$ forest skewer. We find that our network can successfully reproduce IGM conditions with high fidelity across range of instrumental signal-to-noise. These predictions are subsequently translated into the temperature-density plane, facilitating the derivation of reliable constraints on thermal parameters. This allows us to estimate temperature at mean cosmic density, $T_{\rm 0}$ with one sigma confidence $δT_{\rm 0} \sim 1000{\rm K}$ using only one $20$Mpc/h sightline ($Δz\simeq 0.04$) with a typical reionization history. Existing studies utilize redshift pathlength comparable to $Δz\simeq 4$ for similar constraints. We can also provide more stringent constraints on the slope ($1σ$ confidence interval $δ{\rm γ} \lesssim 0.1$) of the IGM temperature-density relation as compared to other traditional approaches. We test the reconstruction on a single high signal-to-noise observed spectrum ($20$ Mpc/h segment), and recover thermal parameters consistent with current measurements. This machine learning approach has the potential to provide accurate yet robust measurements of IGM thermal history at the redshifts in question. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05794 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning the Intergalactic Medium using Lyman-alpha Forest at $ 4 \leq z \leq 5$ Nasir, Fahad Gaikwad, Prakash Davies, Frederick B. Bolton, James S. Puchwein, Ewald Bosman, Sarah E. I. Cosmology and Nongalactic Astrophysics Unveiling the thermal history of the intergalactic medium (IGM) at $4 \leq z \leq 5$ holds the potential to reveal early onset HeII reionization or lingering thermal fluctuations from HI reionization. We set out to reconstruct the IGM gas properties along simulated Lyman-alpha forest data on pixel-by-pixel basis, employing deep Bayesian neural networks. Our approach leverages the Sherwood-Relics simulation suite, consisting of diverse thermal histories, to generate mock spectra. Our convolutional and residual networks with likelihood metric predicts the Ly$α$ optical depth-weighted density or temperature for each pixel in the Ly$α$ forest skewer. We find that our network can successfully reproduce IGM conditions with high fidelity across range of instrumental signal-to-noise. These predictions are subsequently translated into the temperature-density plane, facilitating the derivation of reliable constraints on thermal parameters. This allows us to estimate temperature at mean cosmic density, $T_{\rm 0}$ with one sigma confidence $δT_{\rm 0} \sim 1000{\rm K}$ using only one $20$Mpc/h sightline ($Δz\simeq 0.04$) with a typical reionization history. Existing studies utilize redshift pathlength comparable to $Δz\simeq 4$ for similar constraints. We can also provide more stringent constraints on the slope ($1σ$ confidence interval $δ{\rm γ} \lesssim 0.1$) of the IGM temperature-density relation as compared to other traditional approaches. We test the reconstruction on a single high signal-to-noise observed spectrum ($20$ Mpc/h segment), and recover thermal parameters consistent with current measurements. This machine learning approach has the potential to provide accurate yet robust measurements of IGM thermal history at the redshifts in question. |
| title | Deep Learning the Intergalactic Medium using Lyman-alpha Forest at $ 4 \leq z \leq 5$ |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2404.05794 |