BaesEta2AnisotropyModel#

jeanspy.baes_eta2.BaesEta2AnisotropyModel

See methods and properties for individual lookup pages, or the alphabetical API dictionary to search all classes. The full class contract and existing member anchors are retained below.

class jeanspy.baes_eta2.BaesEta2AnisotropyModel(submodels=None)[source]#

Bases: jeanspy.model_jax.BaesAnisotropyModel

Baes–van Hese anisotropy with eta fixed to 2.

Fixing eta=2 gives

beta(r) = [beta_0 + beta_inf (r/r_a)^2] / [1 + (r/r_a)^2],

and admits the Appell-F1 LOS kernel implemented above. Select DSphModel.sigmalos2(..., solver="kernel", n_kernel=...) to use it. The default solver="auto" selects Abel integration, as for every BaesAnisotropyModel subclass; constructing this class alone does not change that selection. n_kernel controls the specialized kernel quadrature only when the kernel solver is selected.

Notes

Inputs and units. Physical params beta_0, beta_inf and positive r_a (pc); eta is fixed at two. beta/f use radius in pc; kernel uses dimensionless u=r/R and projected radius in pc.

Returns and shape. Dimensionless anisotropy/kernel and integrating factor f.

Validity. This specialized model is not the arbitrary-eta Baes model. Check the stated supported prior envelope.

Errors. Invalid proposals may yield nonfinite values; downstream likelihood rejects invalid forward variance.

Backend. JAX arrays on the configured CPU/GPU, with dtype set before import.

Differentiation. Continuous beta_0/beta_inf/r_a derivatives on the supported fixed-rule path.

Examples. scripts/benchmark_baes_eta2_accuracy.py

Parameters:

submodels (Optional[Mapping[str, 'Model']])

required_param_names: tuple[str, ...] = ('beta_0', 'beta_inf', 'r_a')#
beta(r_pc, *, params)[source]#

Return dimensionless eta=2 Baes anisotropy with radius-array shape.

r_pc and the positive params[‘r_a’] are in pc; beta_0 and beta_inf are dimensionless. Continuous parameters support JAX differentiation within the class domain; this helper does not validate that domain.

Parameters:
Return type:

jax.Array

f(r_pc, *, params)[source]#

Return the eta=2 radial Jeans integrating factor on JAX arrays.

Positive r_pc and params[‘r_a’] are in pc; beta_0 and beta_inf are dimensionless. The arbitrary factor normalization cancels in the Jeans solution. Shape follows r_pc; valid continuous parameters are differentiable. The origin requires a model-dependent limit.

Parameters:
Return type:

jax.Array

kernel(u, R_pc, *, params, n_kernel=96)[source]#

Return the dimensionless eta=2 LOS kernel with broadcast u/R_pc shape.

u=r/R must be at least one and R_pc must be positive, in pc. params supplies beta_0, beta_inf and r_a. n_kernel is a static integer node count; refine it to check convergence. Physical-parameter derivatives follow baes_eta2_kernel_jax and its documented domain restrictions.

Parameters:
Return type:

jax.Array

required_models: Mapping[str, type[jeanspy.model_jax.Model]] = {}#
sampling_identity()#

Model configuration without the derived compilation cache.