diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py
index b58436b8..d8e6369a 100644
--- a/src/jimgw/prior.py
+++ b/src/jimgw/prior.py
@@ -240,6 +240,108 @@ def log_prob(self, x: dict) -> Float:
         return jnp.log(x[self.naming[2]] ** 2 * jnp.sin(x[self.naming[0]]))
 
 
+class Alignedspin(Prior):
+
+    """
+    Prior distribution for the aligned (z) component of the spin.
+
+    This assume the prior distribution on the spin magnitude to be uniform in [0, amax]
+    with its orientation uniform on a sphere
+
+    p(chi) = -log(|chi| / amax) / 2 / amax
+
+    This is useful when comparing results between an aligned-spin run and
+    a precessing spin run.
+
+    See (A7) of https://arxiv.org/abs/1805.10457.
+    """
+
+    amax: float = 0.99
+    chi_axis: Array = jnp.linspace(0, 1, num=1000)
+    cdf_vals: Array = jnp.linspace(0, 1, num=1000)
+
+    def __init__(
+        self,
+        amax: float,
+        naming: list[str],
+        transforms: dict[tuple[str, Callable]] = {},
+    ):
+        super().__init__(naming, transforms)
+        assert isinstance(amax, float), "xmin must be a float"
+        assert self.n_dim == 1, "Alignedspin needs to be 1D distributions"
+        self.amax = amax
+
+        # build the interpolation table for the ppf of the one-sided distribution
+        chi_axis = jnp.linspace(1e-31, self.amax, num=1000)
+        cdf_vals = -chi_axis * (jnp.log(chi_axis / self.amax) - 1.) / self.amax
+        self.chi_axis = chi_axis
+        self.cdf_vals = cdf_vals
+
+    def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> dict:
+        """
+        Sample from the Alignedspin distribution.
+
+        for chi > 0;
+        p(chi) = -log(chi / amax) / amax  # halved normalization constant
+        cdf(chi) = -chi * (log(chi / amax) - 1) / amax
+
+        Since there is a pole at chi=0, we will sample with the following steps
+        1. Map the samples with quantile > 0.5 to positive chi and negative otherwise
+        2a. For negative chi, map the quantile back to [0, 1] via q -> 2(0.5 - q)
+        2b. For positive chi, map the quantile back to [0, 1] via q -> 2(q - 0.5)
+        3. Map the quantile to chi via the ppf by checking against the table
+           built during the initialization
+        4. add back the sign
+
+        Parameters
+        ----------
+        rng_key : jax.random.PRNGKey
+            A random key to use for sampling.
+        n_samples : int
+            The number of samples to draw.
+
+        Returns
+        -------
+        samples : dict
+            Samples from the distribution. The keys are the names of the parameters.
+
+        """
+        q_samples = jax.random.uniform(
+            rng_key, (n_samples,), minval=0., maxval=1.
+        )
+        # 1. calculate the sign of chi from the q_samples
+        sign_samples = jnp.where(
+            q_samples >= 0.5,
+            jnp.zeros_like(q_samples) + 1.,
+            jnp.zeros_like(q_samples) - 1.,
+        )
+        # 2. remap q_samples
+        q_samples = jnp.where(
+            q_samples >=0.5,
+            2 * (q_samples - 0.5),
+            2 * (0.5 - q_samples),
+        )
+        # 3. map the quantile to chi via interpolation
+        samples = jnp.interp(
+            q_samples,
+            self.cdf_vals,
+            self.chi_axis,
+        )
+        # 4. add back the sign
+        samples *= sign_samples
+
+        return self.add_name(samples[None])
+
+    def log_prob(self, x: dict) -> Float:
+        variable = x[self.naming[0]]
+        log_p = jnp.where(
+            (variable >= self.amax) | (variable <= -self.amax),
+            jnp.zeros_like(variable) - jnp.inf,
+            jnp.log(-jnp.log(jnp.absolute(variable) / self.amax) / 2. / self.amax),
+        )
+        return log_p
+
+
 class Powerlaw(Prior):
 
     """