{"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/clusternets-a-deep-learning-approach-to-probe","title":"Clusternets: A deep learning approach to probe clustering dark energy","arxiv_id":"2308.03517","date":"2023-08-07","proceeding":null,"authors":["Amirmohammad Chegeni","Farbod Hassani","Alireza Vafaei Sadr","Nima Khosravi","Martin Kunz"],"abstract":"Machine Learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) trained on matter density snapshots to distinguish clustering Dark Energy (DE) from the cosmological constant scenario and to detect the speed of sound ($c_s$) associated with clustering DE. We compare the CNN results with those from a Random Forest (RF) algorithm trained on power spectra. Varying the dark energy equation of state parameter $w_{\\rm{DE}}$ within the range of -0.7 to -0.99, while keeping $c_s^2 = 1$, we find that the CNN approach results in a significant improvement in accuracy over the RF algorithm. The improvement in classification accuracy can be as high as $40\\%$ depending on the physical scales involved. We also investigate the ML algorithms' ability to detect the impact of the speed of sound by choosing $c_s^2$ from the set $\\{1, 10^{-2}, 10^{-4}, 10^{-7}\\}$ while maintaining a constant $w_{\\rm DE}$ for three different cases: $w_{\\rm DE} \\in \\{-0.7, -0.8, -0.9\\}$. Our results suggest that distinguishing between various values of $c_s^2$ and the case where $c_s^2=1$ is challenging, particularly at small scales and when $w_{\\rm{DE}}\\approx -1$. However, as we consider larger scales, the accuracy of $c_s^2$ detection improves. Notably, the CNN algorithm consistently outperforms the RF algorithm, leading to an approximate $20\\%$ enhancement in $c_s^2$ detection accuracy in some cases.","url_abs":"https://arxiv.org/abs/2308.03517v2","url_pdf":"https://arxiv.org/pdf/2308.03517v2.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":"clusternets-a-deep-learning-approach-to-probe","repo_url":"https://github.com/am-chegeni/clusternets","is_official":1,"mentioned_in_paper":1,"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}