diff --git a/csiborgtools/flow/flow_model.py b/csiborgtools/flow/flow_model.py
index d2443e6..7bb358d 100644
--- a/csiborgtools/flow/flow_model.py
+++ b/csiborgtools/flow/flow_model.py
@@ -25,7 +25,6 @@ from abc import ABC, abstractmethod
 
 import numpy as np
 from astropy import units as u
-from astropy.coordinates import SkyCoord, angular_separation
 from astropy.cosmology import FlatLambdaCDM, z_at_value
 from interpax import interp1d
 from jax import jit
@@ -39,90 +38,19 @@ from tqdm import trange
 
 from ..params import SPEED_OF_LIGHT
 from ..utils import fprint
+from .cosmography import (dist2redshift, distmod2dist, distmod2dist_gradient,
+                          distmod2redshift, gradient_redshift2dist)
 from .selection import toy_log_magnitude_selection
-from .void_model import interpolate_void, load_void_data
+from .void_model import (angular_distance_from_void_axis, interpolate_void,
+                         load_void_data)
 
 H0 = 100  # km / s / Mpc
 
 
 ###############################################################################
-#                           JAX Flow model                                    #
+#                       Various flow utilities                                #
 ###############################################################################
 
-def dist2redshift(dist, Omega_m, h=1.):
-    """
-    Convert comoving distance to cosmological redshift if the Universe is
-    flat and z << 1.
-    """
-    eta = 3 * Omega_m / 2
-    return 1 / eta * (1 - (1 - 2 * 100 * h * dist / SPEED_OF_LIGHT * eta)**0.5)
-
-
-def redshift2dist(z, Omega_m):
-    """
-    Convert cosmological redshift to comoving distance if the Universe is
-    flat and z << 1.
-    """
-    q0 = 3 * Omega_m / 2 - 1
-    return SPEED_OF_LIGHT * z / (2 * H0) * (2 - z * (1 + q0))
-
-
-def gradient_redshift2dist(z, Omega_m):
-    """
-    Gradient of the redshift to comoving distance conversion if the Universe is
-    flat and z << 1.
-    """
-    q0 = 3 * Omega_m / 2 - 1
-    return SPEED_OF_LIGHT / H0 * (1 - z * (1 + q0))
-
-
-def distmod2dist(mu, Om0):
-    """
-    Convert distance modulus to distance in `Mpc / h`. The expression is valid
-    for a flat universe over the range of 0.00001 < z < 0.1.
-    """
-    term1 = jnp.exp((0.443288 * mu) + (-14.286531))
-    term2 = (0.506973 * mu) + 12.954633
-    term3 = ((0.028134 * mu) ** (
-        ((0.684713 * mu)
-         + ((0.151020 * mu) + (1.235158 * Om0))) - jnp.exp(0.072229 * mu)))
-    term4 = (-0.045160) * mu
-    return (-0.000301) + (term1 * (term2 - (term3 - term4)))
-
-
-def distmod2dist_gradient(mu, Om0):
-    """
-    Calculate the derivative of comoving distance in `Mpc / h` with respect to
-    the distance modulus. The expression is valid for a flat universe over the
-    range of 0.00001 < z < 0.1.
-    """
-    term1 = jnp.exp((0.443288 * mu) + (-14.286531))
-    dterm1 = 0.443288 * term1
-
-    term2 = (0.506973 * mu) + 12.954633
-    dterm2 = 0.506973
-
-    term3 = ((0.028134 * mu)**(((0.684713 * mu) + ((0.151020 * mu) + (1.235158 * Om0))) - jnp.exp(0.072229 * mu)))  # noqa
-    ln_base = jnp.log(0.028134) + jnp.log(mu)
-    exponent = 0.835733 * mu + 1.235158 * Om0 - jnp.exp(0.072229 * mu)
-    exponent_derivative = 0.835733 - 0.072229 * jnp.exp(0.072229 * mu)
-    dterm3 = term3 * ((1 / mu) * exponent + exponent_derivative * ln_base)
-
-    term4 = (-0.045160) * mu
-    dterm4 = -0.045160
-
-    return (dterm1 * (term2 - (term3 - term4))
-            + term1 * (dterm2 - (dterm3 - dterm4)))
-
-
-def distmod2redshift(mu, Om0):
-    """
-    Convert distance modulus to redshift, assuming `h = 1`. The expression is
-    valid for a flat universe over the range of 0.00001 < z < 0.1.
-    """
-    return jnp.exp(((0.461108 * mu) - ((0.022187 * Om0) + (((0.022347 * mu)** (12.631788 - ((-6.708757) * Om0))) + 19.529852))))  # noqa
-
-
 def project_Vext(Vext_x, Vext_y, Vext_z, RA_radians, dec_radians):
     """Project the external velocity vector onto the line of sight."""
     cos_dec = jnp.cos(dec_radians)
@@ -298,17 +226,12 @@ class BaseFlowValidationModel(ABC):
         rLG_grid *= h
         rLG_min, rLG_max = rLG_grid.min(), rLG_grid.max()
         rgrid_min, rgrid_max = 0, 250
-        fprint(f"setting radial grid from {rLG_min} to {rLG_max} Mpc.")
+        fprint(f"setting radial grid from {rLG_min} to {rLG_max} Mpc / h.")
         rgrid_max *= h
 
-        # Get angular separation (in degrees) of each object from the model
-        # axis.
-        model_axis = SkyCoord(l=117, b=4, frame='galactic', unit='deg').icrs
-        coords = SkyCoord(ra=RA, dec=dec, unit='deg').icrs
-
-        phi = angular_separation(coords.ra.rad, coords.dec.rad,
-                                 model_axis.ra.rad, model_axis.dec.rad)
-        phi = jnp.asarray(phi * 180 / np.pi, dtype=jnp.float32)
+        # Get angular separation of each object from the model axis.
+        phi = angular_distance_from_void_axis(RA, dec)
+        phi = jnp.asarray(phi, dtype=jnp.float32)
 
         if kind == "density":
             void_grid = jnp.log(void_grid)
@@ -836,7 +759,6 @@ class PV_LogLikelihood(BaseFlowValidationModel):
         else:
             raise ValueError(f"Unknown kind: `{self.kind}`.")
 
-        # h = field_calibration_params["h"]
         # ----------------------------------------------------------------
         # 2. Log-likelihood of the true distance and observed redshifts.
         # The marginalisation of the true distance can be done numerically.
@@ -989,7 +911,7 @@ def PV_validation_model(models, distmod_hyperparams_per_model,
     # We sample the components of Vext with a uniform prior, which means
     # there is a |Vext|^2 prior, we correct for this so that the sampling
     # is effecitvely uniformly in magnitude of Vext and angles.
-    if "Vext" in field_calibration_params:
+    if "Vext" in field_calibration_params and not field_calibration_hyperparams["no_Vext"]:  # noqa
         ll -= jnp.log(jnp.sum(field_calibration_params["Vext"]**2))
 
     for n in range(len(models)):