{"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/distinguishing-coupled-dark-energy-models","title":"Distinguishing Coupled Dark Energy Models with Neural Networks","arxiv_id":"2411.04058","date":"2024-11-06","proceeding":null,"authors":["L. W. K. Goh","I. Ocampo","S. Nesseris","V. Pettorino"],"abstract":"We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\\Lambda$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishing between cosmological models. For our dataset, we generated $f\\sigma_8(z)$ growth-rate observables that simulate a realistic Stage IV galaxy survey-like setup for both $\\Lambda$CDM and a tomographic CDE model for various values of the model parameters. We then optimised and trained our NN with \\texttt{Optuna}, aiming to avoid overfitting and to maximise the accuracy of the trained model. We conducted our analysis for both a binary classification, comparing between $\\Lambda$CDM and a CDE model where only one tomographic coupling bin is activated, and a multi-class classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with $>86\\%$ accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated and, at a $100\\%$ confidence level, detect the $\\Lambda$CDM model. For the multi-class classification task, we find that the NN performs adequately well at distinguishing $\\Lambda$CDM, a CDE model with low-redshift coupling, and a model with high-redshift coupling, with 99\\%, 79\\%, and 84\\% accuracy, respectively. By leveraging the power of machine learning, our pipeline can be a useful tool for analysing growth-rate data and maximising the potential of current surveys to probe for deviations from general relativity.","url_abs":"https://arxiv.org/abs/2411.04058v2","url_pdf":"https://arxiv.org/pdf/2411.04058v2.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":"distinguishing-coupled-dark-energy-models","repo_url":"https://github.com/IndiraOcampo/Growth_LSS_model_selection_CDE","is_official":1,"mentioned_in_paper":0,"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}