Causality Sum Rules in Conventional Scattering Matrices

• URL: http://arxiv.org/abs/2608.10427v1
• Date: Tue, 11 Aug 2026 03:18:47 GMT
• Title: Causality Sum Rules in Conventional Scattering Matrices
• Abstract summary: We show that causality sum rules can be written directly in the conventional scattering matrix.<n>We define a domain-delayed matrix that preserves real-frequency passivity while restoring the causal time origin.<n>Our work directly connects fundamental causality theory with experimentally accessible scattering data.
• Score: 8.754936685779551
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scattering matrices are the standard experimental and computational description of photonic and electromagnetic devices. Passivity is explicit in the conventional incoming-outgoing matrix, whereas causality sum rules are usually formulated only after transforming the response into auxiliary variables. Here we show that these rules can be written directly in the conventional scattering matrix by removing the time advance introduced by the reference domain. Using the earliest-arrival delay of each channel, we define a domain-delayed matrix that preserves real-frequency passivity while restoring the causal time origin. Under explicit analyticity, transparency, and regularity assumptions, this matrix becomes a Schur function, enabling a Cayley-Herglotz construction. The resulting projected and determinant bounds constrain coherent channel superpositions and aggregate multichannel loss. The framework recovers Rozanov's absorber limit and spherical-multipole sum rules, while extending causality bounds to measurable quantities including insertion loss, suppressed singular-value channels, and conditional lossless delay-bandwidth trade-offs. Our work directly connects fundamental causality theory with experimentally accessible scattering data. The initial theoretical route is autonomously explored by Qiushi Engine, an AI research system for open-ended scientific discovery, and subsequently verified, refined, and developed by the authors, demonstrating a hybrid AI-human discovery workflow.

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