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

Nasir, Fahad; Gaikwad, Prakash; Davies, Frederick B; Bolton, James S; Puchwein, Ewald; Bosman, Sarah E I

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 (L y α) 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 αoptical depth-weighted density or temperature for each pixel in the L y αforest 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 0, with 1 σ confidence, δT 0 _ 1000 K, using only one 20 h −1 c Mpc sightline ( _z _ 0 . 04) with a typical reionization history. Existing studies utilize redshift path-length comparable to _z _ 4 for similar constraints. We can also provide more stringent constraints on the slope (1 σ 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 (20 h −1 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.

Citation

Nasir, F., Gaikwad, P., Davies, F. B., Bolton, J. S., Puchwein, E., & Bosman, S. E. I. (2024). Deep learning the intergalactic medium using lyman-alpha forest at 4 ≤ z ≤ 5. Monthly Notices of the Royal Astronomical Society, 534(2), 1299–1316. https://doi.org/10.1093/mnras/stae2153

• Journal Article Type: Article
• Acceptance Date: Sep 13, 2024
• Online Publication Date: Sep 17, 2024
• Publication Date: Oct 1, 2024
• Deposit Date: Oct 8, 2024
• Publicly Available Date: Sep 17, 2024
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 534
• Issue: 2
• Pages: 1299–1316
• DOI: https://doi.org/10.1093/mnras/stae2153
• Keywords: methods: numerical
• Public URL: https://nottingham-repository.worktribe.com/output/39730996
• Publisher URL: https://academic.oup.com/mnras/article/534/2/1299/7759720?login=true