{"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/ole-online-learning-emulation-in-cosmology","title":"OLÉ -- Online Learning Emulation in Cosmology","arxiv_id":"2503.13183","date":"2025-03-17","proceeding":null,"authors":["Sven Günther","Lennart Balkenhol","Christian Fidler","Ali Rida Khalife","Julien Lesgourgues","Markus R. Mosbech","Ravi Kumar Sharma"],"abstract":"In this work, we present OL\\'E, a new online learning emulator for use in cosmological inference. The emulator relies on Gaussian Processes and Principal Component Analysis for efficient data compression and fast evaluation. Moreover, OL\\'E features an automatic error estimation for optimal active sampling and online learning. All training data is computed on-the-fly, making the emulator applicable to any cosmological model or dataset. We illustrate the emulator's performance on an array of cosmological models and data sets, showing significant improvements in efficiency over similar emulators without degrading accuracy compared to standard theory codes. We find that OL\\'E is able to considerably speed up the inference process, increasing the efficiency by a factor of $30-350$, including data acquisition and training. Typically the runtime of the likelihood code becomes the computational bottleneck. Furthermore, OL\\'E emulators are differentiable; we demonstrate that, together with the differentiable likelihoods available in the $\\texttt{candl}$ library, we can construct a gradient-based sampling method which yields an additional improvement factor of 4. OL\\'E can be easily interfaced with the popular samplers $\\texttt{MontePython}$ and $\\texttt{Cobaya}$, and the Einstein-Boltzmann solvers $\\texttt{CLASS}$ and $\\texttt{CAMB}$. OL\\'E is publicly available at https://github.com/svenguenther/OLE .","url_abs":"https://arxiv.org/abs/2503.13183v1","url_pdf":"https://arxiv.org/pdf/2503.13183v1.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":"ole-online-learning-emulation-in-cosmology","repo_url":"https://github.com/svenguenther/ole","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2503.13183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13183"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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