Struct PrecomputedTransmissionModel 

pub struct PrecomputedTransmissionModel {
    pub cross_sections: Arc<Vec<Vec<f64>>>,
    pub density_indices: Arc<Vec<usize>>,
    pub instrument: Option<Arc<InstrumentParams>>,
    pub resolution_plan: Option<Arc<ResolutionPlan>>,
    pub sparse_cubature_plan: Option<Arc<SparseEmpiricalCubaturePlan>>,
    pub sparse_scalar_plan: Option<Arc<ScalarSurrogatePlan>>,
    pub layout: Arc<WorkingGridLayout>,
}

Transmission model backed by precomputed Doppler-broadened cross-sections.

The expensive physics steps (resonance → σ(E), Doppler broadening) are computed once and stored. Each evaluate() call performs Beer-Lambert and, when instrument is present, resolution broadening on the total transmission:

T(E) = R ⊗ exp(−Σᵢ nᵢ · σ_{D,i}(E))

Issue #442: resolution broadening is applied to T(E) after Beer-Lambert, not to σ(E) before.

Fields

cross_sections: Arc<Vec<Vec<f64>>>

density_indices: Arc<Vec<usize>>

Mapping: params[density_indices[i]] is the density of isotope i.

Wrapped in Arc so that parallel pixel loops can share one copy via cheap reference-count increments instead of deep-cloning per pixel.

instrument: Option<Arc<InstrumentParams>>

Instrument resolution parameters. When Some, resolution broadening is applied to the total transmission after Beer-Lambert in evaluate().

resolution_plan: Option<Arc<ResolutionPlan>>

sparse_cubature_plan: Option<Arc<SparseEmpiricalCubaturePlan>>

Optional sparse empirical cubature plan.

When the plan is present AND its target_energies match this model’s energy grid AND cubature.k() == n_density_params AND no temperature / energy-scale fitting is active, the evaluate() / analytical_jacobian() fast path calls cubature.forward_and_jacobian(n) directly instead of exp(-Σ n σ) + apply_resolution. Any guard failure falls back to the exact path, so installing a plan cannot change results unless every guard passes.

sparse_scalar_plan: Option<Arc<ScalarSurrogatePlan>>

Optional scalar (k = 1) surrogate plan.

Mutually exclusive with sparse_cubature_plan in practice — the cubature dispatch fires only for k ≥ 2 and the scalar plan only for k == 1. The type alias ScalarSurrogatePlan = ScalarChebyshevPlan is kept as a stable public name so a future scalar surrogate can swap in without touching this field or any dispatch call site. Chebyshev-in-density was picked over Lanczos Gauss quadrature after a real-VENUS bench-off (Chebyshev won on both the accuracy and wall-time axes; see nereids_physics::surrogate module docs).

layout: Arc<WorkingGridLayout>

Trait Implementations

impl FitModel for PrecomputedTransmissionModel

fn analytical_jacobian( &self, params: &[f64], free_param_indices: &[usize], y_current: &[f64], ) -> Option<FlatMatrix>

Analytical Jacobian for the Beer-Lambert transmission model.

Without resolution: T(E) = exp(-Σᵢ nᵢ · σᵢ(E)) ∂T/∂nᵢ = -σᵢ(E) · T(E)

With resolution (R is a linear operator): T_obs(E) = R[T](E) = R[exp(-Σᵢ nᵢ · σᵢ)](E) ∂T_obs/∂nᵢ = R[-σᵢ(E) · T(E)]

For grouped isotopes sharing density parameter N_g: ∂T_obs/∂N_g = R[-(Σ_{i∈g} σᵢ(E)) · T(E)]

fn evaluate(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>

Evaluate the model for the given parameters.

impl ForwardModel for PrecomputedTransmissionModel

fn predict(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>

Predict model output for the given parameter vector.

fn jacobian( &self, params: &[f64], free_param_indices: &[usize], y_current: &[f64], ) -> Option<Vec<Vec<f64>>>

Analytical Jacobian (column-major layout).

fn n_data(&self) -> usize

Number of data points in the model output.

fn n_params(&self) -> usize

Number of parameters (total, including fixed).