{"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/deep-learning-of-desi-mock-spectra-to-find","title":"Deep Learning of DESI Mock Spectra to Find Damped Lyα Systems","arxiv_id":"2201.00827","date":"2022-01-03","proceeding":null,"authors":["Ben Wang","Jiaqi Zou","Zheng Cai","J. Xavier Prochaska","Zechang Sun","Jiani Ding","Andreu Font-Ribera","Alma Gonzalez","Hiram K. Herrera-Alcantar","Vid Irsic","Xiaojing Lin","David Brooks","Solène Chabanier","Roger de Belsunce","Nathalie Palanque-Delabrouille","Gregory Tarle","Zhimin Zhou"],"abstract":"We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Ly$\\alpha$ systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99$\\%$ for spectra which have signal-to-noise (S/N) above 5 per pixel. Classification accuracy is the rate of correct classifications. This accuracy remains above 97$\\%$ for lower signal-to-noise (S/N) $\\approx1$ spectra. This CNN model provides estimations for redshift and HI column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 per pixel. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of Baryon Acoustic Oscillation (BAO) is investigated. The cosmological fitting parameter result for BAO has less than $0.61\\%$ difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above $1.7\\%$. We also compared the performance of CNN and Gaussian Process (GP) model. Our improved CNN model has moderately 14$\\%$ higher purity and 7$\\%$ higher completeness than an older version of GP code, for S/N $>$ 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by $24\\%$ less standard deviation. A credible DLA catalog for DESI main survey can be provided by combining these two algorithms.","url_abs":"https://arxiv.org/abs/2201.00827v1","url_pdf":"https://arxiv.org/pdf/2201.00827v1.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":"deep-learning-of-desi-mock-spectra-to-find","repo_url":"https://github.com/cosmodesi/desi-dlas","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}