Observability Limits in Coherence-Constrained Planetary Reconstruction: The Role of Population Accumulation

This paper examines the limits of backward reconstruction for planetary and astrophysical systems under coherence-constrained inference frameworks, with particular focus on the Synthesis & Estimation Architecture (SEA). Unlike Earth system reconstruction, which benefits from stratified material archives, space-based reconstruction relies primarily on snapshot observability, raising fundamental questions of identifiability and residual uncertainty. The work formalizes the SEA reconstruction problem as an ensemble of admissible historical trajectories constrained by present-day observables and global coherence conditions. Within this framework, it identifies and classifies eight residual uncertainty classes specific to space and planetary systems, ordered by fundamentality and characterized by their diagnostic signatures—what varies across admissible histories, what remains invariant in observables, and what types of measurements could, in principle, reduce the uncertainty. The paper distinguishes uncertainty classes that are structurally irreducible under snapshot-only observability from those that can be partially constrained through orthogonal observables or through statistical accumulation across large system populations. It introduces the concept of population coherence profiles as an operational way to summarize ensemble structure across many reconstructions without implying unique historical solutions for individual systems. Rather than proposing new formation or migration models, this work clarifies the epistemic role of coherence-constrained reconstruction in space contexts. It provides a principled framework for interpreting ensemble outputs, avoiding over-interpretation of apparent precision, and guiding observational priorities for current and future surveys. The emphasis is on observability limits and information loss, not methodological deficiency, positioning the paper as a domain-general methods and interpretation reference rather than a model-validation study.