Deep learning the intergalactic medium using Lyman-alpha forest at 4 ≤ z ≤ 5
Unveiling the thermal history of the intergalactic medium (IGM) at
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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
October 2024
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2024, Jahrgang: 534, Heft: 2, Pages: 1299-1316 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stae2153 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/mnras/stae2153
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| Verfasserangaben: | Fahad Nasir, Prakash Gaikwad, Frederick B Davies, James S Bolton, Ewald Puchwein and Sarah E.I. Bosman |
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| 245 | 1 | 0 | |a Deep learning the intergalactic medium using Lyman-alpha forest at 4 ≤ z ≤ 5 |c Fahad Nasir, Prakash Gaikwad, Frederick B Davies, James S Bolton, Ewald Puchwein and Sarah E.I. Bosman |
| 246 | 3 | 3 | |a Deep learning the intergalactic medium using Lyman-alpha forest at 4 less or equal to zeta less or equal to 5 |
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| 520 | |a Unveiling the thermal history of the intergalactic medium (IGM) at |4 \le z \le 5$ holds the potential to reveal early onset He ii reionization or lingering thermal fluctuations from H i reionization. We set out to reconstruct the IGM gas properties along simulated Lyman-alpha (Lyα) forest data on pixel-by-pixel basis, employing deep 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 predict the Ly$\rm {\alpha }$ optical depth-weighted density or temperature for each pixel in the Ly$\rm {\alpha }$ forest skewer. We find that our network can successfully reproduce IGM conditions with high fidelity across range of instrumental signal-to-noise ratio. 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 1σ confidence, $\delta {T_{\rm 0}} \lesssim 1000 \ {\rm K}$, using only one $20 \ h^{-1} \, {\rm cMpc}$ sightline ($\Delta z\simeq 0.04$) with a typical reionization history. Existing studies utilize redshift path-length comparable to $\Delta z\simeq 4$ for similar constraints. We can also provide more stringent constraints on the slope ($1\sigma$ confidence interval, $\delta {\rm \gamma } \lesssim 0.1$) 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 ($20 \ h^{-1}\,{\rm cMpc}$ 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. | ||
| 700 | 1 | |a Gaikwad, Prakash |e VerfasserIn |4 aut | |
| 700 | 1 | |a Davies, Frederick B. |e VerfasserIn |0 (DE-588)1192316126 |0 (DE-627)1670632318 |4 aut | |
| 700 | 1 | |a Bolton, James S |e VerfasserIn |4 aut | |
| 700 | 1 | |a Puchwein, Ewald |e VerfasserIn |4 aut | |
| 700 | 1 | |a Bosman, Sarah |e VerfasserIn |0 (DE-588)1297436172 |0 (DE-627)185378124X |4 aut | |
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