{"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/inverted-eemd-a-robust-method-to-identify","title":"Inverted EEMD: a robust method to identify narrow absorption features form spectral data and cubes","arxiv_id":"2410.14611","date":"2024-10-18","proceeding":null,"authors":["Zhenzhen He","Guang-Xing Li"],"abstract":"Extracting information from complex data is a challenge shared by multiple frontiers of modern astrophysical research. Among those, analyzing spectra cubes, where the emission is mapped in the position-position-velocity space is a difficult task given the vast amount of information contained within. The cubes often contain a superposition of emissions and absorptions, where extracting absorption signatures is often necessary. One example is the extraction of narrow absorption structures in HI 21 cm emission spectra. These HI self-absorption (HISA) clouds trace the cold HI gas in interstellar space. We introduce an automatic and robust method called the \\emph{inverted EEMD} method to extract narrow features from spectral cubes. Our method is based on the EEMD method, an established method to decompose 1d signals. The method is robust and parameter-free, making it useful in analyzing spectral cubes containing localized absorption signals of different types. The inverted-EEMD method is suitable for the analysis of spectral cubes where it can produce a cube containing the absorption signal and one containing the unabsorbed signal, where cold clouds can be identified as coherent regions in the absorption map. A Python implementation of the method is available at \\url{https://github.com/zhenzhen-research/inverted_eemd_map}.","url_abs":"https://arxiv.org/abs/2410.14611v1","url_pdf":"https://arxiv.org/pdf/2410.14611v1.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":"inverted-eemd-a-robust-method-to-identify","repo_url":"https://github.com/zhenzhen-research/inverted_eemd_map","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}