{"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/the-baryon-cycle-project-bycycle-identifying","title":"The BarYon CYCLE Project (ByCycle): Identifying and Localizing MgII Metal Absorbers with Machine Learning","arxiv_id":"2305.17970","date":"2023-05-29","proceeding":null,"authors":["Roland Szakacs","Céline Péroux","Dylan Nelson","Martin A. Zwaan","Daniel Grün","Simon Weng","Alejandra Y. Fresco","Victoria Bollo","Benedetta Casavecchia"],"abstract":"The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph will offer new prospects of using a massive sample of $\\sim 1$ million high spectral resolution ($R$ = 20,000) background quasars to map the circumgalactic metal content of foreground galaxies (observed at $R$ = 4000 - 7000), as traced by metal absorption. Such large surveys require specialized analysis methodologies. In the absence of early data, we instead produce synthetic 4MOST high-resolution fibre quasar spectra. To do so, we use the TNG50 cosmological magnetohydrodynamical simulation, combining photo-ionization post-processing and ray tracing, to capture MgII ($\\lambda2796$, $\\lambda2803$) absorbers. We then use this sample to train a Convolutional Neural Network (CNN) which searches for, and estimates the redshift of, MgII absorbers within these spectra. For a test sample of quasar spectra with uniformly distributed properties ($\\lambda_{\\rm{MgII,2796}}$, $\\rm{EW}_{\\rm{MgII,2796}}^{\\rm{rest}} = 0.05 - 5.15$ \\AA, $\\rm{SNR} = 3 - 50$), the algorithm has a robust classification accuracy of 98.6 per cent and a mean wavelength accuracy of 6.9 \\AA. For high signal-to-noise spectra ($\\rm{SNR > 20}$), the algorithm robustly detects and localizes MgII absorbers down to equivalent widths of $\\rm{EW}_{\\rm{MgII,2796}}^{\\rm{rest}} = 0.05$ \\AA. For the lowest SNR spectra ($\\rm{SNR=3}$), the CNN reliably recovers and localizes EW$_{\\rm{MgII,2796}}^{\\rm{rest}}$ $\\geq$ 0.75 \\AA\\, absorbers. This is more than sufficient for subsequent Voigt profile fitting to characterize the detected MgII absorbers. We make the code publicly available through GitHub. Our work provides a proof-of-concept for future analyses of quasar spectra datasets numbering in the millions, soon to be delivered by the next generation of surveys.","url_abs":"https://arxiv.org/abs/2305.17970v1","url_pdf":"https://arxiv.org/pdf/2305.17970v1.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":"the-baryon-cycle-project-bycycle-identifying","repo_url":"https://github.com/astroland93/qso-mag2net","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}