Deep learning the intergalactic medium using lyman-alpha forest at 4 ≤ z ≤ 5

IF 4.7 3区 物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS
Fahad Nasir, Prakash Gaikwad, Frederick B Davies, James S Bolton, Ewald Puchwein, Sarah E I Bosman
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Abstract

Unveiling the thermal history of the intergalactic medium (IGM) at 4 ≤ z ≤ 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 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 predicts the Lyα optical depth-weighted density or temperature for each pixel in the Lyα 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, T0 , with one sigma confidence, $\delta {\rm T_{\rm 0}}\,$≲ 1000K, using only one 20h−1cMpc sightline (Δz ≃ 0.04) with a typical reionization history. Existing studies utilize redshift pathlength comparable to Δz ≃ 4 for similar constraints. We can also provide more stringent constraints on the slope (1σ confidence interval, δγ ≲ 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 (20h−1cMpc 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.
利用 4 ≤ z ≤ 5 的莱曼-阿尔法森林深度学习星系间介质
揭示 4 ≤ z ≤ 5 星系间介质(IGM)的热历史,有可能揭示早期开始的 He ii 再电离或 H i 再电离的残余热波动。我们开始利用深度神经网络,沿着模拟的莱曼-阿尔法森林数据,逐像素地重建IGM气体特性。我们的方法利用由不同热历史组成的 Sherwood-Relics 模拟套件来生成模拟光谱。我们的卷积和残差网络利用似然度量预测了 Lyα 森林串联图中每个像素的 Lyα 光学深度加权密度或温度。我们发现,在仪器信噪比范围内,我们的网络能够成功地高保真地再现 IGM 条件。这些预测随后被转化为温度-密度平面,从而有助于推导出可靠的热参数约束。这使得我们能够仅利用一条具有典型再电离历史的20h-1cMpc视线(Δz ≃0.04),以一个西格玛的置信度($\delta {\rm T_{\rm 0}})来估计平均宇宙密度T0的温度。现有的研究利用与Δz ≃ 4相当的红移路径长度来获得类似的约束条件。与其他传统方法相比,我们还能对IGM温度-密度关系的斜率(1σ置信区间,δγ ≲0.1)提供更严格的约束。我们在单个高信噪比观测光谱(20h-1cMpc 段)上测试了重构,恢复的热参数与当前的测量结果一致。这种机器学习方法有可能为相关红移下的IGM热历史提供准确而稳健的测量结果。
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来源期刊
CiteScore
9.10
自引率
37.50%
发文量
3198
审稿时长
3 months
期刊介绍: Monthly Notices of the Royal Astronomical Society is one of the world''s leading primary research journals in astronomy and astrophysics, as well as one of the longest established. It publishes the results of original research in positional and dynamical astronomy, astrophysics, radio astronomy, cosmology, space research and the design of astronomical instruments.
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