{"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/fast-high-fidelity-lyman-a-forests-with","title":"Fast, high-fidelity Lyman $α$ forests with convolutional neural networks","arxiv_id":"2106.12662","date":"2021-06-23","proceeding":null,"authors":["Peter Harrington","Mustafa Mustafa","Max Dornfest","Benjamin Horowitz","Zarija Lukić"],"abstract":"Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we train a convolutional neural network to use a cheaper N-body-only simulation to reconstruct the baryon hydrodynamic variables (density, temperature, and velocity) on scales relevant to the Lyman-$\\alpha$ (Ly$\\alpha$) forest, using data from Nyx simulations. We show that our method enables rapid estimation of these fields at a resolution of $\\sim$20kpc, and captures the statistics of the Ly$\\alpha$ forest with much greater accuracy than existing approximations. Because our model is fully-convolutional, we can train on smaller simulation boxes and deploy on much larger ones, enabling substantial computational savings. Furthermore, as our method produces an approximation for the hydrodynamic fields instead of Ly$\\alpha$ flux directly, it is not limited to a particular choice of ionizing background or mean transmitted flux.","url_abs":"https://arxiv.org/abs/2106.12662v1","url_pdf":"https://arxiv.org/pdf/2106.12662v1.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":"abstracts"},"code_links":[{"paper_slug":"fast-high-fidelity-lyman-a-forests-with","repo_url":"https://github.com/NERSC/sc21-dl-tutorial","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}