{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-the-intergalactic-medium-using","title":"Deep Learning the Intergalactic Medium using Lyman-alpha Forest at $ 4 \\leq z \\leq 5$","arxiv_id":"2404.05794","date":"2024-04-08","proceeding":null,"authors":["Fahad Nasir","Prakash Gaikwad","Frederick B. Davies","James S. Bolton","Ewald Puchwein","Sarah E. I. Bosman"],"abstract":"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$\\alpha$ optical depth-weighted density or temperature for each pixel in the Ly$\\alpha$ 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 $\\delta T_{\\rm 0} \\sim 1000{\\rm K}$ using only one $20$Mpc/h sightline ($\\Delta z\\simeq 0.04$) with a typical reionization history. Existing studies utilize redshift pathlength 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 ($20$ 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.","url_abs":"https://arxiv.org/abs/2404.05794v1","url_pdf":"https://arxiv.org/pdf/2404.05794v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"deep-learning-the-intergalactic-medium-using","repo_url":"https://github.com/nicenustian/bh2igm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}