Struct PrecomputedTransmissionModel 

pub struct PrecomputedTransmissionModel {
    pub cross_sections: Arc<Vec<Vec<f64>>>,
    pub density_indices: Arc<Vec<usize>>,
    pub energies: Option<Arc<Vec<f64>>>,
    pub instrument: Option<Arc<InstrumentParams>>,
}

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.

Construct via nereids_physics::transmission::broadened_cross_sections, then wrap in Arc so the same precomputed data is shared read-only across all rayon worker threads.

Fields

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

Doppler-broadened cross-sections σ_D(E) per isotope, shape [n_isotopes][n_energies].

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.

Kept pub (not pub(crate)) because the Python bindings (nereids-python) construct and access this field directly.

energies: Option<Arc<Vec<f64>>>

Energy grid (eV), required for resolution broadening. None when resolution is disabled — Beer-Lambert only.

instrument: Option<Arc<InstrumentParams>>

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

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).