OsipkovMerrittModel

jeanspy.model_jax.OsipkovMerrittModel

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class jeanspy.model_jax.OsipkovMerrittModel(submodels=None)

Bases: jeanspy.model_jax.AnisotropyModel

JAX Osipkov–Merritt anisotropy with an analytic LOS kernel.

The only physical parameter is positive r_a in pc. The profile is beta(r)=r**2/(r**2+r_a**2), with integrating factor f(r)=1+r**2/r_a**2. beta/f preserve radius shape. The dimensionless kernel broadcasts u=r/R >= 1 with positive projected R_pc in pc.

kernel has no numerical-order or backend argument; it evaluates a closed form in JAX and supports physical-parameter gradients in smooth valid regions. Elementary formulas do not validate every input domain; invalid radii/scales can produce nonfinite values. Runtime JAX precision/platform configuration applies. See examples/docs_jax_spherical.py.

Parameters:

submodels (Dict[str, jeanspy.model_jax.Model])

required_param_names: tuple[str, ...] = ('r_a',)

beta(r_pc, *, params)

Return beta(r)=r^2/(r^2+r_a^2).

Parameters:

• r_pc (jax.Array)

• params (Mapping[str, Any])

Return type:

jax.Array

f(r_pc, *, params)

Return f(r)=1+r^2/r_a^2 from the note table.

Parameters:

• r_pc (jax.Array)

• params (Mapping[str, Any])

Return type:

jax.Array

kernel(u, R_pc, *, params)

Closed-form K(u) for Osipkov-Merritt anisotropy.

Uses the analytical expression equivalent to the note/CLUMPY formula, with u_a=r_a/R and u=r/R.

Parameters:

• u (jax.Array)

• R_pc (jax.Array)

• params (Mapping[str, Any])

Return type:

jax.Array

required_models: Mapping[str, type[jeanspy.model_jax.Model]] = {}

sampling_identity()

Model configuration without the derived compilation cache.

submodels: Dict[str, jeanspy.model_jax.Model]