{"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/power-spectrum-emulators-from-neural-networks","title":"Power Spectrum Emulators from Neural Networks and Tree-Based Methods","arxiv_id":"2506.07514","date":"2025-06-09","proceeding":null,"authors":["Andrei Lazanu"],"abstract":"We use two subsets of 2000 and 1000 Quijote simulations to build two power spectrum emulators, allowing for fast computations of the non-linear matter power spectrum. The first emulator is built in terms of seven cosmological parameters: the matter and baryon fraction of the energy density of the Universe $\\Omega_m$ and $\\Omega_b$, the reduced Hubble constant $h$, the scalar spectral index $n_s$, the amplitude of matter density fluctuations $\\sigma_8$, the total neutrino mass $M_{\\nu}$ and the dark energy equation of state parameter $w$, on scales $k \\in [0.015,1.8]\\,h/ \\rm{Mpc^{-1}}$. The power spectra can be directly determined at redshifts 0, 0.5, 1, 2 and 3, while for intermediate redshifts these can be interpolated. The second emulator is based on five cosmological parameters, $\\Omega_m$, $h$, $n_s$, $\\sigma_8$ and the amplitude of equilateral non-Gaussianity $f_{\\rm NL}^{\\rm eq}$, at redshifts 0, 0.503, 0.733, 0.997 for $k \\in [0.015,1.8]\\,h/ \\rm{Mpc^{-1}}$. The emulators are built on machine learning techniques. In both cases we have investigated both neural networks and tree-based methods and we have shown that the best accuracy is obtained for a neural network with two hidden layers. Both emulators achieve a root-mean-squared relative error of less then 5\\% for all the redshifts considered on the scales discussed.","url_abs":"https://arxiv.org/abs/2506.07514v1","url_pdf":"https://arxiv.org/pdf/2506.07514v1.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":"power-spectrum-emulators-from-neural-networks","repo_url":"https://github.com/andreilazanu/power_spectrum_emulators","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","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}