Learning the averaged history of an inhomogeneous universe from its present day density field

[2608.27962] Learning the averaged history of an inhomogeneous universe from its present day density field Skip to main content Search Submit Donate Log in Astrophysics > Cosmology and Nongalactic Astrophysics arXiv:2608.27962 (astro-ph) [Submitted on 28 Aug 2026] Title: Learning the averaged history of an inhomogeneous universe from its present day density field Authors: Jonas Broe Bendtsen , Sofie Marie Koksbang View PDF HTML (experimental) Abstract: In this work, we investigate whether machine learning can be used to infer averaged cosmological quantities directly from the present day matter density distribution. Using an implementation of the simplified silent universe framework, we generate a dataset consisting of 92610 independent relativistic, simplified cosmological simulations spanning a range of initial conditions and average cosmological parameters. We train a convolutional neural network to take the present-time matter density field as input and predict the averaged matter, cosmological constant, curvature, and kinematical backreaction density parameters as well as the Hubble parameter, at both initial and present time. The network achieves coefficients of determination exceeding 0.9 for all predicted quantities, with error distributions showing that only a small fraction of predictions reach percent-level errors or above. Although our use of the simplified silent universe approximation precludes direct application of the trained model to observational data, the results provide a proof-of-principle that neural networks can successfully recover averaged cosmological properties, including backreaction, at different epochs, simply from the late-time matter distribution. More broadly, our findings demonstrate that information about the averaged cosmological history of a universe is encoded in its present-time density field and can be extracted using machine learning techniques. This opens the possibility of applying similar approaches to more realistic cosmological simulations and observational probes such as N-body simulations and weak-lensing maps, ultimately providing a new avenue for constraining the large-scale evolution of the Universe. Comments: 11 pages and 5 captioned figures. Prepared for submission to The Open journal of Astrophysics Subjects: Cosmology and Nongalactic Astrophysics (astro-ph.CO) Cite as: arXiv:2608.27962 [astro-ph.CO] (or arXiv:2608.27962v1 [astro-ph.CO] for this version) https://doi.org/10.48550/arXiv.2608.27962 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sofie Marie Koksbang [ view email ] [v1] Fri, 28 Aug 2026 06:11:47 UTC (3,101 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.CO < prev | next > new | recent | 2026-08 Change to browse by: astro-ph References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Learning the averaged history of an inhomogeneous universe from its present day density field#783149
• Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology#986298
• CosmoFlow: Using Deep Learning to Learn the Universe at Scale#620154
• Physically Consistent Parameter Inference: Transparent Machine Learning Emulation in High Energy Physics and Cosmology#366263
• Tracing the Cosmic Origins: Machine Learning Reconstruction of the Primordial Density Field from EoR Observations#972336