SEA-G: A Coherence Diagnostic for Galactic Architecture in Large Surveys

SEA-G: A Coherence Diagnostic for Galactic Architecture in Large Surveys | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 February 2026 V1 Latest version Share on SEA-G: A Coherence Diagnostic for Galactic Architecture in Large Surveys Author : Peter Brunzelle 0009-0005-7109-6745 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177143662.27581868/v1 110 views 57 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract We present SEA-G (Synthesis & Estimation Architecture-Galaxies), an assumption-light diagnostic framework for comparative analysis of galactic architecture in large surveys. Each galaxy is represented by a minimal latent architectural state derived from structural, kinematic, stellar population, and starformation observables. Internal consistency among these dimensions is quantified by a coverage-weighted coherence metric, Cg, enabling regime classification and identification of architecturally disrupted or transitional systems. We apply SEA-G to 512,436 low-redshift galaxies (z < 0.1) from the DESI DR1 Bright Galaxy Survey and to 1,982 galaxies from SDSS-IV MaNGA DR17 with resolved kinematics. The coherence distribution is stable under normalization and threshold variation, with a low-coherence fraction of 17±3% in DESI and 11 ± 2% in MaNGA. Low-coherence systems are significantly enriched in known transitional populations (green valley, post-starburst, kinematically misaligned), with odds ratios OR = 1.9 ± 0.2 from logistic regression controlling for stellar mass and star-formation rate. Gaussian mixture modeling favors a two-component description of the coherence distribution (∆BIC = 18.4 ± 2.1). SEA-G does not infer causal evolutionary pathways. Instead, it provides a reproducible diagnostic layer that integrates heterogeneous observables into a single interpretable scalar, complementing forward modeling and enabling systematic triage in next-generation galaxy surveys. This paper forms part of a broader series developing the Synthesis & Estimation Architecture (SEA) as a domain-general framework for coherence-based diagnostics and observability-limited inference across astrophysical systems. Supplementary Material File (sea_g__a_coherence_diagnostic_for_galactic_architecture_in_large_surveys (2).pdf) Download 272.78 KB Information & Authors Information Version history V1 Version 1 18 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords architectural disruption coherence diagnostics galactic architecture galaxy transition regimes large-scale galaxy surveys latent state modeling sea statistical consistency metrics survey-scale classification Authors Affiliations Peter Brunzelle 0009-0005-7109-6745 [email protected] Independent Researcher View all articles by this author Metrics & Citations Metrics Article Usage 110 views 57 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Peter Brunzelle. SEA-G: A Coherence Diagnostic for Galactic Architecture in Large Surveys. Authorea . 18 February 2026. 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