Authentication of high-value cultural heritage objects relies primarily on legacy empirical frameworks developed prior to modern statistical decision theory, digital identity management, and multi-factor authentication (MFA). While conservation science provides diverse analytical techniques to evaluate material, structural, and chemical properties, these investigations typically operate as independent silos rather than components of a unified identity framework. Consequently, combining provenance, connoisseurship, and scientific analysis into a coherent protocol with quantifiable confidence levels remains an unaddressed challenge. This paper reformulates cultural heritage authentication as a statistical identity problem. Building on standards-based hyperspectral authentication and physical multi-factor frameworks, we formalize a new class of authentication factors called physicometric factors, defined as the measurement and statistical characterization of invariant physical properties of inanimate objects. These stable descriptors arise from an object's material composition, structural organization, fabrication history, and physical response to controlled interrogation. Examples include spectral reflectance, micro-topographic surface structure, chemical signatures, refractive index transitions, and fabrication-derived entropy patterns. To implement this framework, we introduce OMASIE (Optical Multiscale Aperture Synthesizing Invariant Extractor), an architecture employing structured illumination, coded apertures, and computational reconstruction to translate object responses into high-dimensional identity vectors suitable for statistical comparison. This methodology resolves measurement degeneracy, including metamerism and related ambiguities, through dimensional expansion, covariance-aware evidence fusion, and identity vector comparison. Beyond immediate verification, aggregating these physical identity vectors establishes a structured knowledge base to support retrospective scholarship, workshop attribution, and conservation monitoring.