Scale-Dependent Clustering Suppression from Late-Time Dark Matter Pressure: A CLASS Boltzmann Code Implementation and Validation Protocol

This technical report presents a complete CLASS Boltzmann code implementation of a pressure-bearing dark matter model derived from the BigSnap scalar field framework. The model is characterized by a time-dependent dark matter equation of state w_dm(a) = −r·a³/(4Ω_R0) and an effective perturbation sound speed c_s²(a) = |w_dm(a)|^γ, where r = λ_Λ/λ_m is a coupling ratio and γ ∈ [1, 2] parameterizes unknown scalar field microphysics. The c_s²(a) ∝ a³γ time dependence produces a late-time, scale-dependent Jeans suppression: σ₈-scale modes enter pressure support at z ~ 1–2 while CMB primary anisotropy scales remain unaffected throughout. This is a structural consequence of the model, not a tuned coincidence. This deposit provides: The exact perturbations.c modification required (a single added term to the CDM velocity equation) A mode-entry prediction plot (Figure 1) constituting a pre-committed, falsifiable prediction for CLASS output before any run is executed A four-function Python unit test suite (bigsnap_unit_tests.py) that gates σ₈ computation behind diagnostic prerequisites, preventing silent false positives A two-case contrast protocol defining the priority runs (null control and signal case) A complete parameter scan specification over r ∈ {10⁻³, 10⁻⁴, 10⁻⁵} and γ ∈ {1.0, 1.25, 1.5, 1.75, 2.0} The pipeline enforces a strict diagnostic hierarchy: scale independence, three-way onset consistency, and velocity divergence checks must all pass before any scalar observable is reported. A resolution test using a convergence ratio Δ₁/Δ₂ guards against artificial suppression from integration error. The CLASS runs are pending at the time of deposit. This deposit records the pre-committed predictions and validation protocol before numerical results exist, establishing that the predictions were fixed in advance of the data. This document is self-contained for CLASS implementation. Theoretical derivations of the framework are in the companion document: Snider, D., "The Big Snap," (2026).