Semi-Analytic Evaluation of the STFO Screening Kernel and Mode-Dependent Coefficients

We present a semi-analytic evaluation of screening in the Scalar Temporal Field Ontology (STFO) using a concrete benchmark model for the temporal-field action. Building on prior work establishing the Green’s-function structure of screening and the linearized response operator, we adopt explicit model choices P(X)=X+αX² and U(τ)=½m²τ² to derive the weak-gradient response kernel. The resulting operator reduces to Helmholtz form with screening scale μ_eff = m / √(1+2αX₀), yielding a Yukawa kernel for the induced temporal response. Using hydrogen-like benchmark mode profiles, we compute screening coefficients for the 1s, 2s, and 2p modes and confirm the STFO mode-ordering hierarchy. This work provides the first concrete benchmark model linking STFO action parameters to mode-dependent screening behavior.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-22
DOI
https://doi.org/10.5281/zenodo.19155946
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

Semi-Analytic Evaluation of the STFO Screening Kernel and Mode-Dependent Coefficients

Cale Scott Howe
Zenodo (CERN European Organization for Nuclear Research)
Neural Networks and Reservoir Computing
article

Semi-Analytic Evaluation of the STFO Screening Kernel and Mode-Dependent Coefficients

Cale Scott Howe
article en

Abstract

We present a semi-analytic evaluation of screening in the Scalar Temporal Field Ontology (STFO) using a concrete benchmark model for the temporal-field action. Building on prior work establishing the Green’s-function structure of screening and the linearized response operator, we adopt explicit model choices P(X)=X+αX² and U(τ)=½m²τ² to derive the weak-gradient response kernel. The resulting operator reduces to Helmholtz form with screening scale μ_eff = m / √(1+2αX₀), yielding a Yukawa kernel for the induced temporal response. Using hydrogen-like benchmark mode profiles, we compute screening coefficients for the 1s, 2s, and 2p modes and confirm the STFO mode-ordering hierarchy. This work provides the first concrete benchmark model linking STFO action parameters to mode-dependent screening behavior.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 15%
Neural Networks and Reservoir Computing
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