diff --git a/clmm/__init__.py b/clmm/__init__.py
index f679b53cd..07ef6a591 100644
--- a/clmm/__init__.py
+++ b/clmm/__init__.py
@@ -26,4 +26,4 @@
 )
 from . import support
 
-__version__ = "1.14.5"
+__version__ = "1.14.6"
diff --git a/clmm/theory/func_layer.py b/clmm/theory/func_layer.py
index f2b465411..b9dec4cad 100644
--- a/clmm/theory/func_layer.py
+++ b/clmm/theory/func_layer.py
@@ -1,6 +1,7 @@
 """@file func_layer.py
 Main functions to encapsule oo calls
 """
+
 # pylint: disable=too-many-lines
 # pylint: disable=invalid-name
 # Thin functonal layer on top of the class implementation of CLMModeling .
@@ -108,6 +109,8 @@ def compute_surface_density(
     halo_profile_model="nfw",
     massdef="mean",
     alpha_ein=None,
+    r_mis=None,
+    mis_from_backend=False,
     verbose=False,
     use_projected_quad=False,
     validate_input=True,
@@ -119,6 +122,14 @@ def compute_surface_density(
 
     where :math:`\rho(r)` is the 3d density profile.
 
+    If the `r_mis` keyword is specified, this function computes the miscentered surface 
+    density instead as
+
+    .. math::
+        \Sigma_{\rm mis}(R, R_{\rm mis}) = \frac{1}{2\pi}\int_0^{2\pi} d\theta \,
+        \Sigma\left(\sqrt{R^2 + R_{\rm mis}^2 - 2 R R_{\rm mis} \cos\theta} \right)\;,
+
+
     Parameters
     ----------
     r_proj : array_like
@@ -152,6 +163,13 @@ def compute_surface_density(
         Option only available for the NumCosmo and CCL backends.
         If None, use the default value of the backend. (0.25 for the NumCosmo backend and a
         cosmology-dependent value for the CCL backend.)
+
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`
+    mis_from_backend : bool, optional
+        If True, use the projected surface density from the backend for miscentering
+        calculations. If False, use the (faster) CLMM exact analytical 
+        implementation instead. (Default: False)
     verbose : boolean, optional
         If True, the Einasto slope (alpha_ein) is printed out. Only available for the NC and CCL
         backends.
@@ -181,10 +199,12 @@ def compute_surface_density(
     _modeling_object.set_mass(mdelta)
     if halo_profile_model == "einasto" or alpha_ein is not None:
         _modeling_object.set_einasto_alpha(alpha_ein)
-    if halo_profile_model == "einasto" and _modeling_object.backend=="ccl":
+    if halo_profile_model == "einasto" and _modeling_object.backend == "ccl":
         _modeling_object.set_projected_quad(use_projected_quad)
 
-    sigma = _modeling_object.eval_surface_density(r_proj, z_cl, verbose=verbose)
+    sigma = _modeling_object.eval_surface_density(
+        r_proj, z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend, verbose=verbose
+    )
 
     _modeling_object.validate_input = True
     return sigma
@@ -200,6 +220,8 @@ def compute_mean_surface_density(
     halo_profile_model="nfw",
     massdef="mean",
     alpha_ein=None,
+    r_mis=None,
+    mis_from_backend=False,
     verbose=False,
     validate_input=True,
 ):
@@ -239,6 +261,12 @@ def compute_mean_surface_density(
     alpha_ein : float, optional
         If `halo_profile_model=='einasto'`, set the value of the Einasto slope. Option only
         available for the NumCosmo backend
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`
+    mis_from_backend : bool, optional
+        If True, use the projected surface density from the backend for miscentering
+        calculations. If False, use the (faster) CLMM exact analytical 
+        implementation instead. (Default: False)
     verbose : boolean, optional
         If True, the Einasto slope (alpha_ein) is printed out. Only available for the NC and CCL
         backends.
@@ -266,7 +294,9 @@ def compute_mean_surface_density(
     if alpha_ein is not None:
         _modeling_object.set_einasto_alpha(alpha_ein)
 
-    sigma_bar = _modeling_object.eval_mean_surface_density(r_proj, z_cl, verbose=verbose)
+    sigma_bar = _modeling_object.eval_mean_surface_density(
+        r_proj, z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend, verbose=verbose
+    )
 
     _modeling_object.validate_input = True
     return sigma_bar
@@ -282,6 +312,8 @@ def compute_excess_surface_density(
     halo_profile_model="nfw",
     massdef="mean",
     alpha_ein=None,
+    r_mis=None,
+    mis_from_backend=False,
     verbose=False,
     validate_input=True,
 ):
@@ -323,6 +355,12 @@ def compute_excess_surface_density(
         Option only available for the NumCosmo and CCL backends.
         If None, use the default value of the backend. (0.25 for the NumCosmo backend and a
         cosmology-dependent value for the CCL backend.)
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`
+    mis_from_backend : bool, optional
+        If True, use the projected surface density from the backend for miscentering
+        calculations. If False, use the (faster) CLMM exact analytical 
+        implementation instead. (Default: False)
     verbose : boolean, optional
         If True, the Einasto slope (alpha_ein) is printed out. Only available for the NC and CCL
         backends.
@@ -344,7 +382,9 @@ def compute_excess_surface_density(
     if halo_profile_model == "einasto" or alpha_ein is not None:
         _modeling_object.set_einasto_alpha(alpha_ein)
 
-    deltasigma = _modeling_object.eval_excess_surface_density(r_proj, z_cl, verbose=verbose)
+    deltasigma = _modeling_object.eval_excess_surface_density(
+        r_proj, z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend, verbose=verbose
+    )
 
     _modeling_object.validate_input = True
     return deltasigma
diff --git a/clmm/theory/miscentering.py b/clmm/theory/miscentering.py
new file mode 100644
index 000000000..09cfdc1b4
--- /dev/null
+++ b/clmm/theory/miscentering.py
@@ -0,0 +1,251 @@
+"""@file miscentering.py
+Model functions with miscentering
+"""
+# Functions to model halo profiles
+
+import numpy as np
+from scipy.integrate import quad, dblquad, tplquad
+
+
+def integrand_surface_density_nfw(theta, r_proj, r_mis, r_s):
+    r"""Computes integrand for surface mass density with the NFW profile.
+
+    Parameters
+    ----------
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+
+    Returns
+    -------
+    numpy.ndarray, float
+        2D projected density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    r_norm = np.sqrt(r_proj**2.0 + r_mis**2.0 - 2.0 * r_proj * r_mis * np.cos(theta)) / r_s
+
+    r2m1 = r_norm**2.0 - 1.0
+    if r_norm < 1:
+        sqrt_r2m1 = np.sqrt(-r2m1)
+        res = np.arcsinh(sqrt_r2m1 / r_norm) / (-r2m1) ** (3.0 / 2.0) + 1.0 / r2m1
+    elif r_norm > 1:
+        sqrt_r2m1 = np.sqrt(r2m1)
+        res = -np.arcsin(sqrt_r2m1 / r_norm) / (r2m1) ** (3.0 / 2.0) + 1.0 / r2m1
+    else:
+        res = 1.0 / 3.0
+    return res
+
+
+def integrand_surface_density_einasto(r_par, theta, r_proj, r_mis, r_s, alpha_ein):
+    r"""Computes integrand for surface mass density with the Einasto profile.
+
+    Parameters
+    ----------
+    r_par : array_like
+        Parallel radial position from the cluster center in :math:`M\!pc`.
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+    alpha_ein : float
+        Einasto slope
+
+    Returns
+    -------
+    numpy.ndarray, float
+        2D projected density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    # Projected surface mass density element for numerical integration
+    r_norm = (
+        np.sqrt(r_par**2.0 + r_proj**2.0 + r_mis**2.0 - 2.0 * r_proj * r_mis * np.cos(theta))
+        / r_s
+    )
+
+    return np.exp(-2.0 * (r_norm**alpha_ein - 1.0) / alpha_ein)
+
+
+def integrand_surface_density_hernquist(theta, r_proj, r_mis, r_s):
+    r"""Computes integrand for surface mass density with the Hernquist profile.
+
+    Parameters
+    ----------
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+
+    Returns
+    -------
+    numpy.ndarray, float
+        2D projected density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    r_norm = np.sqrt(r_proj**2.0 + r_mis**2.0 - 2.0 * r_proj * r_mis * np.cos(theta)) / r_s
+
+    r2m1 = r_norm**2.0 - 1.0
+    if r_norm < 1:
+        res = -3 / r2m1**2 + (r2m1 + 3) * np.arcsinh(np.sqrt(-r2m1) / r_norm) / (-r2m1) ** 2.5
+    elif r_norm > 1:
+        res = -3 / r2m1**2 + (r2m1 + 3) * np.arcsin(np.sqrt(r2m1) / r_norm) / (r2m1) ** 2.5
+    else:
+        res = 4.0 / 15.0
+    return res
+
+
+def integrand_mean_surface_density_nfw(theta, r_proj, r_mis, r_s):
+    r"""Computes integrand for mean surface mass density with the NFW profile.
+
+    Parameters
+    ----------
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+
+    Returns
+    -------
+    numpy.ndarray, float
+        Mean surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    return r_proj * integrand_surface_density_nfw(theta, r_proj, r_mis, r_s)
+
+
+def integrand_mean_surface_density_einasto(r_par, theta, r_proj, r_mis, r_s, alpha_ein):
+    r"""Computes integrand for mean surface mass density with the Einasto profile.
+
+    Parameters
+    ----------
+    r_par : array_like
+        Parallel radial position from the cluster center in :math:`M\!pc`.
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+    alpha_ein : float
+        Einasto slope
+
+    Returns
+    -------
+    numpy.ndarray, float
+        Mean surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    return r_proj * integrand_surface_density_einasto(r_par, theta, r_proj, r_mis, r_s, alpha_ein)
+
+
+def integrand_mean_surface_density_hernquist(theta, r_proj, r_mis, r_s):
+    r"""Computes integrand for mean surface mass density with the Hernquist profile.
+
+    Parameters
+    ----------
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+
+    Returns
+    -------
+    numpy.ndarray, float
+        Mean surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    return r_proj * integrand_surface_density_hernquist(theta, r_proj, r_mis, r_s)
+
+
+def integrate_azimuthially_miscentered_surface_density(
+    r_proj, r_mis, integrand, norm, aux_args, extra_integral
+):
+    r"""Integrates azimuthally the miscentered surface mass density kernel.
+
+    Parameters
+    ----------
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    integrand : function
+        Function to be integrated
+    norm : float
+        Normalization value for integral
+    aux_args : list
+        Auxiliary arguments used in the integral
+    extra_integral : bool
+        Additional dimention for the integral
+
+    Returns
+    -------
+    numpy.ndarray, float
+        2D projected density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+    args_list = [(r, r_mis, *aux_args) for r in r_proj]
+    if extra_integral:
+        res = [
+            dblquad(integrand, 0.0, np.pi, 0, np.inf, args=args, epsrel=1e-6)[0]
+            for args in args_list
+        ]
+    else:
+        res = [quad(integrand, 0.0, np.pi, args=args, epsrel=1e-6)[0] for args in args_list]
+
+    res = np.array(res) * norm / np.pi
+    return res
+
+
+def integrate_azimuthially_miscentered_mean_surface_density(
+    r_proj, r_mis, integrand, norm, aux_args, extra_integral
+):
+    r"""Integrates azimuthally the miscentered mean surface mass density kernel.
+
+    Parameters
+    ----------
+    theta : float
+        Angle of polar coordinates of the miscentering direction.
+    r_proj : array_like
+        Projected radial position from the cluster center in :math:`M\!pc`.
+    r_mis : float, optional
+        Projected miscenter distance in :math:`M\!pc`.
+    r_s : array_like
+        Scale radius
+
+    Returns
+    -------
+    numpy.ndarray, float
+        Mean surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+    """
+
+    r_lower = np.zeros_like(r_proj)
+    r_lower[1:] = r_proj[:-1]
+    args = (r_mis, *aux_args)
+
+    if extra_integral:
+        res = [
+            tplquad(integrand, r_low, r_high, 0, np.pi, 0, np.inf, args=args, epsrel=1e-6)[0]
+            for r_low, r_high in zip(r_lower, r_proj)
+        ]
+    else:
+        res = [
+            dblquad(integrand, r_low, r_high, 0, np.pi, args=args, epsrel=1e-6)[0]
+            for r_low, r_high in zip(r_lower, r_proj)
+        ]
+
+    return np.cumsum(res) * norm * 2 / np.pi / r_proj**2
diff --git a/clmm/theory/parent_class.py b/clmm/theory/parent_class.py
index 758db702c..eba8824dc 100644
--- a/clmm/theory/parent_class.py
+++ b/clmm/theory/parent_class.py
@@ -1,6 +1,7 @@
 """@file parent_class.py
 CLMModeling abstract class
 """
+
 # pylint: disable=too-many-lines
 import warnings
 
@@ -8,8 +9,8 @@
 
 # functions for the 2h term
 from scipy.integrate import simpson, quad
-from scipy.special import jv
 from scipy.interpolate import splrep, splev
+from scipy.special import gamma, gammainc, jv
 
 from .generic import (
     compute_reduced_shear_from_convergence,
@@ -26,6 +27,7 @@
     _integ_pzfuncs,
     compute_for_good_redshifts,
 )
+from . import miscentering
 
 
 warnings.filterwarnings("always", module="(clmm).*")
@@ -63,6 +65,7 @@ class CLMModeling:
     z_inf : float
         The value used as infinite redshift
     """
+
     # pylint: disable=too-many-instance-attributes
     # The disable below is added to avoid a pylint error where it thinks CLMMCosmlogy
     # has duplicates since both have many NotImplementedError functions
@@ -301,7 +304,109 @@ def _convert_mass_concentration(
             alpha2=alpha,
         )
 
-    # 3.1. All these functions are for the single plane case
+    # 3.1. Miscentering functions
+
+    def _eval_surface_density_miscentered(self, r_proj, z_cl, r_mis, mis_from_backend):
+        # set integrand function
+        integrand = None
+        if mis_from_backend:
+            integrand = self._integrand_surface_density_mis
+        elif self.halo_profile_model == "nfw":
+            integrand = miscentering.integrand_surface_density_nfw
+        elif self.halo_profile_model == "einasto":
+            integrand = miscentering.integrand_surface_density_einasto
+        elif self.halo_profile_model == "hernquist":
+            integrand = miscentering.integrand_surface_density_hernquist
+        # get aux arguments
+        norm, aux_args = self._miscentering_params(z_cl, mis_from_backend)
+
+        extra_integral = self.halo_profile_model == "einasto" and not mis_from_backend
+        return miscentering.integrate_azimuthially_miscentered_surface_density(
+            r_proj, r_mis, integrand, norm, aux_args, extra_integral
+        )
+
+    def _eval_mean_surface_density_miscentered(self, r_proj, z_cl, r_mis, mis_from_backend):
+        # set integrand function
+        integrand = None
+        if mis_from_backend:
+            integrand = self._integrand_mean_surface_density_mis
+        elif self.halo_profile_model == "nfw":
+            integrand = miscentering.integrand_mean_surface_density_nfw
+        elif self.halo_profile_model == "einasto":
+            integrand = miscentering.integrand_mean_surface_density_einasto
+        elif self.halo_profile_model == "hernquist":
+            integrand = miscentering.integrand_mean_surface_density_hernquist
+        # get aux arguments
+        norm, aux_args = self._miscentering_params(z_cl, mis_from_backend)
+
+        extra_integral = self.halo_profile_model == "einasto" and not mis_from_backend
+        return miscentering.integrate_azimuthially_miscentered_mean_surface_density(
+            r_proj, r_mis, integrand, norm, aux_args, extra_integral
+        )
+
+    def _eval_excess_surface_density_miscentered(self, r_proj, z_cl, r_mis, mis_from_backend):
+        return self._eval_mean_surface_density_miscentered(
+            r_proj, z_cl, r_mis, mis_from_backend
+        ) - self._eval_surface_density_miscentered(r_proj, z_cl, r_mis, mis_from_backend)
+
+    def _integrand_surface_density_mis(self, theta, r_proj, r_mis, z_cl):
+        return self.eval_surface_density(
+            np.sqrt(r_proj**2.0 + r_mis**2.0 - 2 * r_proj * r_mis * np.cos(theta)), z_cl
+        )
+
+    def _integrand_mean_surface_density_mis(self, theta, r_proj, r_mis, z_cl):
+        return r_proj * self._integrand_surface_density_mis(theta, r_proj, r_mis, z_cl)
+
+    def _miscentering_params(self, z_cl, mis_from_backend):
+        params = None
+        if mis_from_backend:
+            params = 1, (z_cl,)
+
+        else:
+            rho_def = self.cosmo.get_rho_m(z_cl)
+            r_s = self.eval_rdelta(z_cl) / self.cdelta
+
+            if self.halo_profile_model == "nfw":
+                rho_s = (
+                    self.delta_mdef
+                    / 3.0
+                    * self.cdelta**3.0
+                    * rho_def
+                    / (np.log(1.0 + self.cdelta) - self.cdelta / (1.0 + self.cdelta))
+                )
+                params = 2 * r_s * rho_s, (r_s,)
+
+            elif self.halo_profile_model == "einasto":
+                alpha_ein = self._get_einasto_alpha(z_cl)
+                rho_s = (
+                    self.delta_mdef
+                    / 3.0
+                    * self.cdelta**3.0
+                    * rho_def
+                    / (
+                        2.0 ** (-3.0 / alpha_ein)
+                        * alpha_ein ** (-1.0 + 3.0 / alpha_ein)
+                        * np.exp(2.0 / alpha_ein)
+                        * gamma(3.0 / alpha_ein)
+                        * gammainc(3.0 / alpha_ein, 2.0 / alpha_ein * self.cdelta**alpha_ein)
+                    )
+                )
+                params = 2 * rho_s, (r_s, alpha_ein)
+
+            elif self.halo_profile_model == "hernquist":
+                rho_s = (
+                    self.delta_mdef
+                    / 3.0
+                    * self.cdelta**3.0
+                    * rho_def
+                    / ((self.cdelta / (1.0 + self.cdelta)) ** 2.0)
+                    * 2
+                )
+                params = r_s * rho_s, (r_s,)
+
+        return params
+
+    # 3.2. All these functions are for the single plane case
 
     def _eval_tangential_shear_core(self, r_proj, z_cl, z_src):
         delta_sigma = self.eval_excess_surface_density(r_proj, z_cl)
@@ -529,31 +634,49 @@ def inv_sigmac(redshift):
 
         return 1.0 / _integ_pzfuncs(pzpdf, pzbins, kernel=inv_sigmac)
 
-    def eval_surface_density(self, r_proj, z_cl, verbose=False):
+    def eval_surface_density(self, r_proj, z_cl, r_mis=None, mis_from_backend=False, verbose=False):
         r"""Computes the surface mass density
 
         Parameters
         ----------
         r_proj : array_like
-            Projected radial position from the cluster center in :math:`M\!pc`.
+            Projected radial position from the cluster center in :math:`M\!pc`
         z_cl: float
             Redshift of the cluster
+        r_mis : float, optional
+            Projected miscenter distance in :math:`M\!pc`
+        mis_from_backend : bool, optional
+            If True, use the projected surface density from the backend for miscentering
+            calculations. If False, use the (faster) CLMM exact analytical
+            implementation instead. (Default: False)
+        verbose : bool, optional
+            If True, the Einasto slope (alpha_ein) is printed out. Only availble for the NC and
+            CCL backends. (Default: False)
 
         Returns
         -------
         numpy.ndarray, float
             2D projected surface density in units of :math:`M_\odot\ Mpc^{-2}`
+
         """
         if self.validate_input:
             validate_argument(locals(), "r_proj", "float_array", argmin=0)
             validate_argument(locals(), "z_cl", float, argmin=0)
+            if r_mis is not None:
+                validate_argument(locals(), "r_mis", float, argmin=0, eqmin=True)
 
         if self.halo_profile_model == "einasto" and verbose:
             print(f"Einasto alpha = {self._get_einasto_alpha(z_cl=z_cl)}")
 
+        if r_mis is not None:
+            return self._eval_surface_density_miscentered(
+                r_proj=r_proj, z_cl=z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend
+            )
         return self._eval_surface_density(r_proj=r_proj, z_cl=z_cl)
 
-    def eval_mean_surface_density(self, r_proj, z_cl, verbose=False):
+    def eval_mean_surface_density(
+        self, r_proj, z_cl, r_mis=None, mis_from_backend=False, verbose=False
+    ):
         r"""Computes the mean value of surface density inside radius `r_proj`
 
         Parameters
@@ -562,22 +685,40 @@ def eval_mean_surface_density(self, r_proj, z_cl, verbose=False):
             Projected radial position from the cluster center in :math:`M\!pc`.
         z_cl: float
             Redshift of the cluster
+        r_mis : float, optional
+            Projected miscenter distance in :math:`M\!pc`.
+        mis_from_backend : bool, optional
+            If True, use the projected surface density from the backend for miscentering
+            calculations. If False, use the (faster) CLMM exact analytical
+            implementation instead. (Default: False)
+        verbose : bool, optional
+            If True, the Einasto slope (alpha_ein) is printed out. Only availble for the NC and
+            CCL backends. (Default: False)
 
         Returns
         -------
         numpy.ndarray, float
-            Excess surface density in units of :math:`M_\odot\ Mpc^{-2}`.
+            Mean surface density in units of :math:`M_\odot\ Mpc^{-2}`.
         """
         if self.validate_input:
             validate_argument(locals(), "r_proj", "float_array", argmin=0)
             validate_argument(locals(), "z_cl", float, argmin=0)
+            if r_mis is not None:
+                validate_argument(locals(), "r_mis", float, argmin=0)
 
         if self.halo_profile_model == "einasto" and verbose:
             print(f"Einasto alpha = {self._get_einasto_alpha(z_cl=z_cl)}")
 
+        if r_mis is not None:
+            return self._eval_mean_surface_density_miscentered(
+                r_proj=r_proj, z_cl=z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend
+            )
+
         return self._eval_mean_surface_density(r_proj=r_proj, z_cl=z_cl)
 
-    def eval_excess_surface_density(self, r_proj, z_cl, verbose=False):
+    def eval_excess_surface_density(
+        self, r_proj, z_cl, r_mis=None, mis_from_backend=False, verbose=False
+    ):
         r"""Computes the excess surface density
 
         Parameters
@@ -586,6 +727,15 @@ def eval_excess_surface_density(self, r_proj, z_cl, verbose=False):
             Projected radial position from the cluster center in :math:`M\!pc`.
         z_cl: float
             Redshift of the cluster
+        r_mis : float, optional
+            Projected miscenter distance in :math:`M\!pc`.
+        mis_from_backend : bool, optional
+            If True, use the projected surface density from the backend for miscentering
+            calculations. If False, use the (faster) CLMM exact analytical
+            implementation instead. (Default: False)
+        verbose : bool, optional
+            If True, the Einasto slope (alpha_ein) is printed out. Only availble for the NC and
+            CCL backends. (Default: False)
 
         Returns
         -------
@@ -595,10 +745,16 @@ def eval_excess_surface_density(self, r_proj, z_cl, verbose=False):
         if self.validate_input:
             validate_argument(locals(), "r_proj", "float_array", argmin=0)
             validate_argument(locals(), "z_cl", float, argmin=0)
+            if r_mis is not None:
+                validate_argument(locals(), "r_mis", float, argmin=0, eqmin=True)
 
         if self.halo_profile_model == "einasto" and verbose:
             print(f"Einasto alpha = {self._get_einasto_alpha(z_cl=z_cl)}")
 
+        if r_mis is not None:
+            return self._eval_excess_surface_density_miscentered(
+                r_proj=r_proj, z_cl=z_cl, r_mis=r_mis, mis_from_backend=mis_from_backend
+            )
         return self._eval_excess_surface_density(r_proj=r_proj, z_cl=z_cl)
 
     def eval_excess_surface_density_2h(
diff --git a/examples/demo_theory_functionality.ipynb b/examples/demo_theory_functionality.ipynb
index 393606340..6a1e45b07 100644
--- a/examples/demo_theory_functionality.ipynb
+++ b/examples/demo_theory_functionality.ipynb
@@ -156,6 +156,17 @@
     "    cosmo=cosmo,\n",
     "    delta_mdef=mass_Delta,\n",
     "    halo_profile_model=density_profile_parametrization,\n",
+    ")\n",
+    "\n",
+    "Sigma_mis = m.compute_surface_density(\n",
+    "    r3d,\n",
+    "    cluster_mass,\n",
+    "    cluster_concentration,\n",
+    "    z_cl,\n",
+    "    cosmo=cosmo,\n",
+    "    delta_mdef=mass_Delta,\n",
+    "    halo_profile_model=density_profile_parametrization,\n",
+    "    r_mis=0.2\n",
     ")"
    ]
   },
@@ -173,6 +184,17 @@
     "    cosmo=cosmo,\n",
     "    delta_mdef=mass_Delta,\n",
     "    halo_profile_model=density_profile_parametrization,\n",
+    ")\n",
+    "\n",
+    "DeltaSigma_mis = m.compute_excess_surface_density(\n",
+    "    r3d,\n",
+    "    cluster_mass,\n",
+    "    cluster_concentration,\n",
+    "    z_cl,\n",
+    "    cosmo=cosmo,\n",
+    "    delta_mdef=mass_Delta,\n",
+    "    halo_profile_model=density_profile_parametrization,\n",
+    "    r_mis=0.2\n",
     ")"
    ]
   },
@@ -346,7 +368,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_profile(r3d, Sigma, \"$\\\\Sigma_{\\\\rm 2d}$\")"
+    "plot_profile(r3d, Sigma, \"$\\\\Sigma_{\\\\rm 2d}$\", label='R_off = No miscentering')\n",
+    "plot_profile(r3d, Sigma_mis, \"$\\\\Sigma_{\\\\rm 2d}$\", label='R_off = 0.2 Mpc')\n",
+    "plt.axvline(0.2, linestyle=':', color='k')\n",
+    "plt.legend()"
    ]
   },
   {
@@ -355,7 +380,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_profile(r3d, DeltaSigma, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\")"
+    "plot_profile(r3d, DeltaSigma, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\", label='No miscentering')\n",
+    "plot_profile(r3d, DeltaSigma_mis, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\", label='R_off = 0.2 Mpc')\n",
+    "plt.axvline(0.2, linestyle=':', color='k')\n",
+    "plt.legend()"
    ]
   },
   {
@@ -543,7 +571,7 @@
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
-   "name": "python3"
+   "name": "wrk"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/examples/demo_theory_functionality_oo.ipynb b/examples/demo_theory_functionality_oo.ipynb
index b0d773f0b..1f979026d 100644
--- a/examples/demo_theory_functionality_oo.ipynb
+++ b/examples/demo_theory_functionality_oo.ipynb
@@ -126,7 +126,11 @@
     "r3d = np.logspace(-2, 2, 100)\n",
     "rho = moo.eval_3d_density(r3d, z_cl)\n",
     "Sigma = moo.eval_surface_density(r3d, z_cl)\n",
+    "# Miscentered Sigma\n",
+    "Sigma_mis = moo.eval_surface_density(r3d, z_cl, r_mis=0.2)\n",
     "DeltaSigma = moo.eval_excess_surface_density(r3d, z_cl)\n",
+    "# Miscentered DeltaSigma\n",
+    "DeltaSigma_mis = moo.eval_excess_surface_density(r3d, z_cl, r_mis=0.2)\n",
     "gammat = moo.eval_tangential_shear(r3d, z_cl, z_src)\n",
     "kappa = moo.eval_convergence(r3d, z_cl, z_src)\n",
     "\n",
@@ -185,7 +189,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_profile(r3d, Sigma, \"$\\\\Sigma_{\\\\rm 2d}$\")"
+    "plot_profile(r3d, Sigma, \"$\\\\Sigma_{\\\\rm 2d}$\", label='R_off = No miscentering')\n",
+    "plot_profile(r3d, Sigma_mis, \"$\\\\Sigma_{\\\\rm 2d}$\", label='R_off = 0.2 Mpc')\n",
+    "plt.axvline(0.2, linestyle=':', color='k')\n",
+    "plt.legend()\n",
+    "plt.ylabel('$\\Sigma$ [$M_\\odot$ Mpc$^{-2}$]')"
    ]
   },
   {
@@ -194,7 +202,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_profile(r3d, DeltaSigma, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\")"
+    "plot_profile(r3d, DeltaSigma, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\", label='No miscentering')\n",
+    "plot_profile(r3d, DeltaSigma_mis, \"$\\\\Delta\\\\Sigma_{\\\\rm 2d}$\", label='R_off = 0.2 Mpc')\n",
+    "plt.axvline(0.2, linestyle=':', color='k')\n",
+    "plt.legend()\n",
+    "plt.ylabel('$\\Delta\\Sigma$ [$M_\\odot$ Mpc$^{-2}$]')\n",
+    "plt.savefig('miscentering.png')"
    ]
   },
   {
@@ -354,9 +367,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "wrk",
    "language": "python",
-   "name": "python3"
+   "name": "wrk"
   },
   "language_info": {
    "codemirror_mode": {
@@ -368,7 +381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/for_dev/explore_miscentering_theory.ipynb b/examples/for_dev/explore_miscentering_theory.ipynb
new file mode 100644
index 000000000..a40cfb6e4
--- /dev/null
+++ b/examples/for_dev/explore_miscentering_theory.ipynb
@@ -0,0 +1,778 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d35cafb6-e104-4fe1-bbb9-4179c70e0391",
+   "metadata": {},
+   "source": [
+    "# Start exploring prediction for miscentered cluster\n",
+    "\n",
+    "This follows the equations from the [cluster-toolkit documentation](https://cluster-toolkit.readthedocs.io/en/latest/source/miscentering.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7b9b7264-f448-49f8-9d7e-f54cb9890293",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "os.environ['CLMM_MODELING_BACKEND'] = 'nc' # here you may choose ccl, nc (NumCosmo) or ct (cluster_toolkit)\n",
+    "\n",
+    "import clmm\n",
+    "from clmm import Cosmology\n",
+    "from scipy.interpolate import interp1d\n",
+    "import scipy.integrate as integrate\n",
+    "from scipy.special import gamma, gammainc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dcad24d8-cf22-4ad8-bdf3-90c2b6ea86b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.3-0.045, Omega_b0=0.045, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "52e06548-7bf7-48cf-8727-e6e28f72cc58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moo = clmm.Modeling(massdef='mean', delta_mdef=200, halo_profile_model='hernquist')\n",
+    "\n",
+    "moo.set_cosmo(cosmo)\n",
+    "moo.set_concentration(5)\n",
+    "moo.set_mass(1.e14)\n",
+    "\n",
+    "z_cl = 0.1\n",
+    "\n",
+    "# for the CCL backend\n",
+    "alpha_ein = 0.25\n",
+    "if moo.halo_profile_model == 'einasto':\n",
+    "    moo.set_einasto_alpha(alpha_ein)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "133564e4-0c4e-4dab-9812-395648f07d75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0.3.dev11+g132cfc1a\n",
+      "1.12.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyccl as ccl\n",
+    "print(ccl.__version__)\n",
+    "print(clmm.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cae13059-4add-4f89-b8f3-00facba3441b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def R_from_true(theta, R, Roff):\n",
+    "    return np.sqrt(R*R + Roff*Roff - 2*R*Roff*np.cos(theta))\n",
+    "\n",
+    "def integrand1(theta, R, Roff):\n",
+    "    return moo.eval_surface_density(R_from_true(theta, R, Roff), z_cl)/(2*np.pi)\n",
+    "\n",
+    "# Sigma exact\n",
+    "def Sigma_mis_exact(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1, 0., 2*np.pi, args=(R, Roff), epsrel=1e-6)[0]\n",
+    "\n",
+    "# Sigma mean exact\n",
+    "def integrand_Sigmamean_exact(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8fd72c1b-2d0b-4bec-8611-bb73b484de0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand1_opt(theta, R, Roff):\n",
+    "    return moo.eval_surface_density(R_from_true(theta, R, Roff), z_cl)\n",
+    "\n",
+    "c200 = moo.cdelta\n",
+    "rho_def = moo.cosmo.get_rho_m(z_cl)\n",
+    "r_s = moo.eval_rdelta(z_cl) / c200\n",
+    "rho_s_nfw = moo.delta_mdef/3.*c200**3.*rho_def/(np.log(1.+c200)-c200/(1.+c200))\n",
+    "rho_s_ein = moo.delta_mdef/3.*c200**3.*rho_def/(2.**(-3./alpha_ein) * alpha_ein**(-1.+3./alpha_ein) * np.exp(2./alpha_ein) * gamma(3./alpha_ein) * gammainc(3./alpha_ein, 2./alpha_ein*c200**alpha_ein))\n",
+    "rho_s_her = moo.delta_mdef/3.*c200**3.*rho_def/((c200/(1. + c200))**2.)*2\n",
+    "\n",
+    "# can do the same for the Hernquist profile too\n",
+    "def integrand1_opt_nfw(theta, R, Roff):\n",
+    "    x = np.sqrt(R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "    x2m1 = x**2. - 1.\n",
+    "    if x < 1:\n",
+    "        sqrt_x2m1 = np.sqrt(-x2m1)\n",
+    "        res = np.arcsinh(sqrt_x2m1/x) / (-x2m1)**(3./2.) + 1./x2m1\n",
+    "    elif x > 1:\n",
+    "        sqrt_x2m1 = np.sqrt(x2m1)\n",
+    "        res = -np.arcsin(sqrt_x2m1/x) / (x2m1)**(3./2.) + 1./x2m1\n",
+    "    else:\n",
+    "        res = 1./3.\n",
+    "    res *= 2. * r_s * rho_s_nfw\n",
+    "    return res\n",
+    "\n",
+    "def integrand1_opt_ein(theta, R, Roff):\n",
+    "    def integrand0(z):\n",
+    "        x = np.sqrt(z**2. + R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "        return np.exp(-2. * (x**alpha_ein - 1.) / alpha_ein)\n",
+    "    return integrate.quad_vec(integrand0, 0., np.inf)[0] * 2. * rho_s_ein\n",
+    "\n",
+    "def integrand1_opt_her(theta, R, Roff):\n",
+    "    x = np.sqrt(R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "    x2m1 = x**2. - 1.\n",
+    "    if x < 1:\n",
+    "        sqrt_x2m1 = np.sqrt(-x2m1)\n",
+    "        res = (-3 / x2m1**2\n",
+    "               + (x2m1+3) * np.arcsinh(sqrt_x2m1/x) / (-x2m1)**2.5)\n",
+    "    elif x > 1:\n",
+    "        sqrt_x2m1 = np.sqrt(x2m1)\n",
+    "        res = (-3 / x2m1**2\n",
+    "               + (x2m1+3) * np.arcsin(sqrt_x2m1/x) / (x2m1)**2.5)\n",
+    "    else:\n",
+    "        res = 4./15.\n",
+    "    res *= r_s * rho_s_her\n",
+    "    return res\n",
+    "\n",
+    "# Sigma exact\n",
+    "def Sigma_mis_exact_opt(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt, 0., np.pi, args=(R, Roff), epsrel=1e-6)[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_nfw(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_nfw, 0., np.pi, args=(R, Roff))[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_ein(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_ein, 0., np.pi, args=(R, Roff))[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_her(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_her, 0., np.pi, args=(R, Roff), epsrel=1e-6)[0]/np.pi\n",
+    "\n",
+    "# Sigma mean exact\n",
+    "def integrand_Sigmamean_exact_opt(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_nfw(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_nfw(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_ein(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_ein(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_her(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_her(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3c1bbb03-47a3-412b-bc51-413001ffb830",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Sigma_mean_mis_exact(R_arr, Roff):\n",
+    "    res=[]\n",
+    "    for i,R in enumerate(R_arr):\n",
+    "        res.append(integrate.quad(integrand_Sigmamean_exact, 0., R, args=(Roff))[0]*2./R/R)\n",
+    "    return np.array(res)\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_nfw(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_nfw, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_ein(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_ein, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_her(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_her, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_trap(R_arr, Roff, regrid=10):\n",
+    "    # use finer grid that R_arr to evaluate integral, for precision purpose. Controld by the regrid parameter.\n",
+    "    new_R_arr = np.logspace(np.log10(1.e-5), np.log10(R_arr.max()), regrid)\n",
+    "\n",
+    "    res = (2./new_R_arr**2) * integrate.cumulative_trapezoid(new_R_arr * Sigma_mis_exact(new_R_arr, Roff), new_R_arr, initial=0)\n",
+    "\n",
+    "    f = interp1d(new_R_arr, res)\n",
+    "    return f(R_arr)\n",
+    "\n",
+    "\n",
+    "def DS_mis_approx(R_arr, Roff, regrid=1000):\n",
+    "    return Sigma_mean_mis_trap(R_arr, Roff, regrid=regrid) - Sigma_mis_exact(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact(R_arr, Roff) - Sigma_mis_exact(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt(R_arr, Roff) - Sigma_mis_exact_opt(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt_nfw(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_nfw(R_arr, Roff) - np.array([Sigma_mis_exact_opt_nfw(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_ein(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_ein(R_arr, Roff) - Sigma_mis_exact_opt_ein(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt_her(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_her(R_arr, Roff) - np.array([Sigma_mis_exact_opt_her(R_, Roff) for R_ in R_arr])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7dcb10aa-1dca-45a5-9473-bb78f3230dc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff = 0.2\n",
+    "R_arr = np.logspace(-2, 1, 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "100519ac-2fdf-418f-b481-9df115211a52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sigma_mis = Sigma_mis_exact_opt(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b3d91664-9f13-48b6-af5a-93fde2faa905",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 6min 38s, sys: 10.6 s, total: 6min 49s\n",
+      "Wall time: 7min 13s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact = DS_mis_exact(R_arr, Roff)\n",
+    "# won't finish (or takes very long) for CCL Einasto"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c603b1f2-5592-40f1-81a5-7762d6d75dd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 12.3 s, sys: 215 ms, total: 12.5 s\n",
+      "Wall time: 12.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt = DS_mis_exact_opt(R_arr, Roff)\n",
+    "# won't finish (or takes very long) for CCL Einasto"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2f94b9fe-3478-4f86-9a72-830a7c147088",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.73 s, sys: 41.6 ms, total: 1.77 s\n",
+      "Wall time: 1.76 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_her = DS_mis_exact_opt_her(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "67ab8d0d-c5a4-4105-9d5a-5c2836a9ffab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.802007734840743e-08"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(DeltaSigma_mis_exact_opt/DeltaSigma_mis_exact-1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2626db91-730f-4d17-8632-48d175bafb20",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0096468228137923e-07"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(DeltaSigma_mis_exact_opt_her/DeltaSigma_mis_exact-1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "9b0ad39c-52a6-49de-a461-b3018453db8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 280 ms, sys: 7.21 ms, total: 287 ms\n",
+      "Wall time: 287 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e2 = DS_mis_approx(R_arr, Roff, regrid=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bbce9edf-dd2a-4193-8c0a-52a16666813b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.51 s, sys: 18.8 ms, total: 1.53 s\n",
+      "Wall time: 1.53 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e3 = DS_mis_approx(R_arr, Roff, regrid=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3499f1e9-593b-4e4e-b6b9-36acc041512c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 19 s, sys: 262 ms, total: 19.2 s\n",
+      "Wall time: 19.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e4 = DS_mis_approx(R_arr, Roff, regrid=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a3193a2e-ff1b-4bd0-b64d-fb27adb78114",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sigma = moo.eval_surface_density(R_arr, z_cl)\n",
+    "DeltaSigma = moo.eval_excess_surface_density(R_arr, z_cl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a4d5642a-350d-41e0-a7d3-3ae097e6c1ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(ncols=3, figsize=(13,4))\n",
+    "axes[0].loglog(R_arr, Sigma, label='No miscentering', marker='+')\n",
+    "axes[0].loglog(R_arr, Sigma_mis, label='Miscentered', marker='+')\n",
+    "axes[0].axvline(Roff, c='k', ls=':')\n",
+    "axes[0].set_xlabel('R[Mpc]')\n",
+    "axes[0].set_ylabel(r'$\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[1].loglog(R_arr, DeltaSigma, label='No miscentering', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_exact, label='Miscentering, exact', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_exact_opt, label='Miscentering, exact(opt)', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e2, label='Miscentered, 1e2', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e3, label='Miscentered, 1e3', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e4, label='Miscentered, 1e4', marker='+')\n",
+    "#axes[1].loglog(R_arr, DeltaSigma_mis_1e5, label='Miscentered, 1e5', marker='+')\n",
+    "axes[1].axvline(Roff, c='k', ls=':')\n",
+    "axes[1].legend()\n",
+    "axes[1].set_xlabel('R[Mpc]')\n",
+    "axes[1].set_ylabel(r'$\\Delta\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e2/DeltaSigma_mis_exact)-1), marker='+', label='approx, 1e2')\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e3/DeltaSigma_mis_exact)-1), marker='+', label='approx, 1e3')\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e4/DeltaSigma_mis_exact)-1), marker='+', label='approx, 1e4')\n",
+    "axes[2].axvline(Roff, c='k', ls=':')\n",
+    "axes[2].set_xlabel('R[Mpc]')\n",
+    "axes[2].set_ylabel(r'$|\\Delta\\Sigma^{\\rm approx}/\\Delta\\Sigma^{\\rm exact}|-1$')\n",
+    "\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "axes[2].legend()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "92da70e2-a9ae-4d7e-9110-fad0df8e4049",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "08a00b78-2ef9-41de-ad32-230190bc899b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Roff_distrib(Roff, Rmis=0.2):\n",
+    "    return np.exp(-Roff/Rmis) * Roff/(Rmis*Rmis)\n",
+    "\n",
+    "def integrand_Sigmastack_mean_exact(Rprime, Roff):\n",
+    "    return Rprime * Sigma_stack_mis_exact(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c3cc788d-2832-44e3-a5f1-0130eded8eea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x1306e3a50>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Roff_arr = np.linspace(0, 20*Roff, 100)\n",
+    "plt.plot(Roff_arr, Roff_distrib(Roff_arr, Rmis=Roff)/Roff_distrib(Roff, Rmis=Roff))\n",
+    "plt.axvline(Roff, linestyle=':')\n",
+    "plt.axvline(10.*Roff, linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "589ec2e4-b368-49d1-ab34-4351088871ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x130b3a1d0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.loglog(Roff_arr, Roff_distrib(Roff_arr, Rmis=Roff)/Roff_distrib(Roff, Rmis=Roff))\n",
+    "plt.axvline(Roff, linestyle=':')\n",
+    "plt.axvline(10.*Roff, linestyle=':')\n",
+    "plt.axhline(1.e-5, linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5b1b7224-4bc9-422d-9994-3f9b6723569a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R_arr_int = np.logspace(-5, 4, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b14dd288-b87f-4697-bec7-77632d66eecf",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def Sigma_mis_interp(R, Roff):\n",
+    "    Sigma_mis = Sigma_mis_exact(R_arr_int, Roff)\n",
+    "    f_sigmamis = interp1d(R_arr_int, Sigma_mis)\n",
+    "    return f_sigmamis(R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1bd45446-1989-4c8a-84a9-1d24c297d593",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand_stack(Roff, R, Rmis):\n",
+    "    return Sigma_mis_interp(R, Roff) * Roff_distrib(Roff, Rmis)\n",
+    "\n",
+    "def integrand_stack_tab(Roff_tab, R, Rmis):\n",
+    "    res = []\n",
+    "    for r in Roff_tab:\n",
+    "        res.append(Sigma_mis_interp(R, r) * Roff_distrib(r, Rmis))\n",
+    "    return np.array(res)\n",
+    "\n",
+    "def Sigma_stack_mis_exact(R_arr, Rmis=3.):\n",
+    "    return integrate.quad_vec(integrand_stack, 1.e-5, 1.e5, args=(R, Rmis))[0]\n",
+    "\n",
+    "def Sigma_stack_mis_simps(R_arr, Rmis, Rinfty_scale=10, ngrid=100):\n",
+    "    Roff_tab = np.linspace(0.,Rmis*Rinfty_scale,ngrid)\n",
+    "    tab = integrand_stack_tab(Roff_tab, R_arr, Rmis)\n",
+    "    return integrate.simpson(y=tab, x=Roff_tab, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d893c16d-8780-48d1-9ece-523b9c19b6cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sigma_mis_stack_simps = Sigma_stack_mis_simps(R_arr, Rmis=Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7d40d1ea-db15-47fe-be2e-b72a1e21f9c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Sigma_mean_mis_stack_trap(R_arr, Rmis, ngrid=100, Rinfty_scale=10, ngrid_sigma=100):\n",
+    "\n",
+    "    new_R_arr = np.logspace(np.log10(1.e-5), np.log10(R_arr.max()), ngrid)\n",
+    "    res = (2./new_R_arr**2) * integrate.cumulative_trapezoid(new_R_arr * Sigma_stack_mis_simps(new_R_arr, Rmis, Rinfty_scale=Rinfty_scale, ngrid=ngrid_sigma), new_R_arr, initial=0)\n",
+    "\n",
+    "    f = interp1d(new_R_arr, res)\n",
+    "    return f(R_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d70641bf-c7a8-4eac-91a6-b601ec6ca77f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff=0.2\n",
+    "R_arr2 = np.logspace(-2, np.log10(20), 50)\n",
+    "\n",
+    "Sigma = moo.eval_surface_density(R_arr2, z_cl)\n",
+    "Sigma_mis = Sigma_mis_exact(R_arr2, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1c8a4bfb-dbe7-4947-8369-3fe5af01bd48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DeltaSigma = moo.eval_excess_surface_density(R_arr2, z_cl)\n",
+    "DeltaSigma_mis = DS_mis_approx(R_arr2, Roff, regrid=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "490efea2-9b5e-41f6-bba7-3074eff099e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sigma_mis_stack = Sigma_stack_mis_simps(R_arr2, Rmis=Roff, Rinfty_scale=10, ngrid=1000)\n",
+    "Sigma_mean_stack_mis_trap = Sigma_mean_mis_stack_trap(R_arr2, Rmis=Roff, ngrid=1000, Rinfty_scale=10, ngrid_sigma=1000)\n",
+    "DeltaSigma_stack_mis = Sigma_mean_stack_mis_trap - Sigma_mis_stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be851a7c-4bbf-43fa-8199-b283f112f27d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4))\n",
+    "axes[0].loglog(R_arr2, Sigma, label='No miscentering', marker='+')\n",
+    "axes[0].loglog(R_arr2, Sigma_mis, label='Miscentered, single', marker='+')\n",
+    "axes[0].loglog(R_arr2, Sigma_mis_stack, label='Miscentered, stack', marker='+')\n",
+    "axes[0].axvline(Roff, c='k', ls=':')\n",
+    "axes[0].set_xlabel('R[Mpc]')\n",
+    "axes[0].set_ylabel(r'$\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[1].loglog(R_arr2, DeltaSigma, label='No miscentering', marker='+')\n",
+    "axes[1].loglog(R_arr2, DeltaSigma_mis, label='Miscentered, single, 1e3', marker='+')\n",
+    "axes[1].loglog(R_arr2, DeltaSigma_stack_mis, label='Miscentered, stack, 1.e3', marker='+')\n",
+    "axes[1].axvline(Roff, c='k', ls=':')\n",
+    "axes[1].legend()\n",
+    "axes[1].set_xlabel('R[Mpc]')\n",
+    "axes[1].set_ylabel(r'$\\Delta\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3bd577a-3392-4148-bb7a-a7a74c32a5ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/for_dev/explore_miscentering_theory_dblquad.ipynb b/examples/for_dev/explore_miscentering_theory_dblquad.ipynb
new file mode 100644
index 000000000..3bf64c9d3
--- /dev/null
+++ b/examples/for_dev/explore_miscentering_theory_dblquad.ipynb
@@ -0,0 +1,1400 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b4b4f7ca-a4d7-414b-a196-6c88fc404805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "os.environ['CLMM_MODELING_BACKEND'] = 'ccl' # here you may choose ccl, nc (NumCosmo) or ct (cluster_toolkit)\n",
+    "\n",
+    "import clmm\n",
+    "from clmm import Cosmology\n",
+    "from scipy.interpolate import interp1d\n",
+    "import scipy.integrate as integrate\n",
+    "from scipy.special import gamma, gammainc\n",
+    "\n",
+    "import timeit\n",
+    "\n",
+    "\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.3-0.045, Omega_b0=0.045, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7f49e1c-68e0-43fb-8904-11fce832078b",
+   "metadata": {},
+   "source": [
+    "# Individual profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5fdd1398-8435-4155-b790-d0cee5a0fc59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moo = clmm.Modeling(massdef='mean', delta_mdef=200, halo_profile_model='nfw')\n",
+    "\n",
+    "moo.set_cosmo(cosmo)\n",
+    "moo.set_concentration(5)\n",
+    "moo.set_mass(1.e14)\n",
+    "\n",
+    "z_cl = 0.1\n",
+    "\n",
+    "# for the CCL backend\n",
+    "alpha_ein = 0.25\n",
+    "if moo.halo_profile_model == 'einasto':\n",
+    "    moo.set_einasto_alpha(alpha_ein)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "386fc83d-f9ab-4d79-933e-b852aff1f4e3",
+   "metadata": {},
+   "source": [
+    "## Original implementation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3c24f225-7401-41af-a4fc-e6ddd67f1991",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def R_from_true(theta, R, Roff):\n",
+    "    return np.sqrt(R*R + Roff*Roff - 2*R*Roff*np.cos(theta))\n",
+    "\n",
+    "def integrand1(theta, R, Roff):\n",
+    "    return moo.eval_surface_density(R_from_true(theta, R, Roff), z_cl)/(2*np.pi)\n",
+    "\n",
+    "# Sigma exact\n",
+    "def Sigma_mis_exact(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1, 0., 2*np.pi, args=(R, Roff), epsrel=1e-6)[0]\n",
+    "\n",
+    "# Sigma mean exact\n",
+    "def integrand_Sigmamean_exact(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63649cea-b3e7-4c30-b034-efc448e286bd",
+   "metadata": {},
+   "source": [
+    "## Optimized implementation + backend independant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "867c59a7-ac1c-4fa0-848d-ffeac1994e66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand1_opt(theta, R, Roff):\n",
+    "    return moo.eval_surface_density(R_from_true(theta, R, Roff), z_cl)\n",
+    "\n",
+    "c200 = moo.cdelta\n",
+    "rho_def = moo.cosmo.get_rho_m(z_cl)\n",
+    "r_s = moo.eval_rdelta(z_cl) / c200\n",
+    "rho_s_nfw = moo.delta_mdef/3.*c200**3.*rho_def/(np.log(1.+c200)-c200/(1.+c200))\n",
+    "rho_s_ein = moo.delta_mdef/3.*c200**3.*rho_def/(2.**(-3./alpha_ein) * alpha_ein**(-1.+3./alpha_ein) * np.exp(2./alpha_ein) * gamma(3./alpha_ein) * gammainc(3./alpha_ein, 2./alpha_ein*c200**alpha_ein))\n",
+    "rho_s_her = moo.delta_mdef/3.*c200**3.*rho_def/((c200/(1. + c200))**2.)*2\n",
+    "\n",
+    "# can do the same for the Hernquist profile too\n",
+    "def integrand1_opt_nfw(theta, R, Roff):\n",
+    "    x = np.sqrt(R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "    x2m1 = x**2. - 1.\n",
+    "    if x < 1:\n",
+    "        sqrt_x2m1 = np.sqrt(-x2m1)\n",
+    "        res = np.arcsinh(sqrt_x2m1/x) / (-x2m1)**(3./2.) + 1./x2m1\n",
+    "    elif x > 1:\n",
+    "        sqrt_x2m1 = np.sqrt(x2m1)\n",
+    "        res = -np.arcsin(sqrt_x2m1/x) / (x2m1)**(3./2.) + 1./x2m1\n",
+    "    else:\n",
+    "        res = 1./3.\n",
+    "    res *= 2. * r_s * rho_s_nfw\n",
+    "    return res\n",
+    "\n",
+    "def integrand1_opt_ein(theta, R, Roff):\n",
+    "    def integrand0(z):\n",
+    "        x = np.sqrt(z**2. + R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "        return np.exp(-2. * (x**alpha_ein - 1.) / alpha_ein)\n",
+    "    return integrate.quad_vec(integrand0, 0., np.inf)[0] * 2. * rho_s_ein\n",
+    "\n",
+    "def integrand1_opt_her(theta, R, Roff):\n",
+    "    x = np.sqrt(R**2. + Roff**2. - 2.*R*Roff*np.cos(theta)) / r_s\n",
+    "    x2m1 = x**2. - 1.\n",
+    "    if x < 1:\n",
+    "        sqrt_x2m1 = np.sqrt(-x2m1)\n",
+    "        res = (-3 / x2m1**2\n",
+    "               + (x2m1+3) * np.arcsinh(sqrt_x2m1/x) / (-x2m1)**2.5)\n",
+    "    elif x > 1:\n",
+    "        sqrt_x2m1 = np.sqrt(x2m1)\n",
+    "        res = (-3 / x2m1**2\n",
+    "               + (x2m1+3) * np.arcsin(sqrt_x2m1/x) / (x2m1)**2.5)\n",
+    "    else:\n",
+    "        res = 4./15.\n",
+    "    res *= r_s * rho_s_her\n",
+    "    return res\n",
+    "\n",
+    "# Sigma exact\n",
+    "def Sigma_mis_exact_opt(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt, 0., np.pi, args=(R, Roff), epsrel=1e-6)[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_nfw(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_nfw, 0., np.pi, args=(R, Roff))[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_ein(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_ein, 0., np.pi, args=(R, Roff))[0]/np.pi\n",
+    "\n",
+    "def Sigma_mis_exact_opt_her(R, Roff):\n",
+    "    return integrate.quad_vec(integrand1_opt_her, 0., np.pi, args=(R, Roff), epsrel=1e-6)[0]/np.pi\n",
+    "\n",
+    "# Sigma mean exact\n",
+    "def integrand_Sigmamean_exact_opt(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_nfw(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_nfw(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_ein(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_ein(Rprime, Roff)\n",
+    "\n",
+    "def integrand_Sigmamean_exact_opt_her(Rprime, Roff):\n",
+    "    return Rprime * Sigma_mis_exact_opt_her(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6a7a51af-fcb7-489c-a8b4-4cc46967a30b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Sigma_mean_mis_exact(R_arr, Roff):\n",
+    "    res=[]\n",
+    "    for i,R in enumerate(R_arr):\n",
+    "        res.append(integrate.quad(integrand_Sigmamean_exact, 0., R, args=(Roff))[0]*2./R/R)\n",
+    "    return np.array(res)\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_nfw(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_nfw, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_ein(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_ein, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_her(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.quad(integrand_Sigmamean_exact_opt_her, R_lower, R, args=(Roff))[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_trap(R_arr, Roff, regrid=10):\n",
+    "    # use finer grid that R_arr to evaluate integral, for precision purpose. Controld by the regrid parameter. \n",
+    "    new_R_arr = np.logspace(np.log10(1.e-5), np.log10(R_arr.max()), regrid)\n",
+    "\n",
+    "    res = (2./new_R_arr**2) * integrate.cumulative_trapezoid(new_R_arr * Sigma_mis_exact(new_R_arr, Roff), new_R_arr, initial=0)\n",
+    "\n",
+    "    f = interp1d(new_R_arr, res)\n",
+    "    return f(R_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21beeb49-62a9-4772-b5e0-66f5f4c73089",
+   "metadata": {},
+   "source": [
+    "## Use dblquad for Sigma_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6decc103-9203-4a61-98bb-4083146f54ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand_dbl_opt(Rp, theta, Roff):\n",
+    "    return Rp * integrand1_opt(theta, Rp, Roff)\n",
+    "\n",
+    "def integrand_dbl_opt_nfw(Rp, theta, Roff):\n",
+    "    return Rp * integrand1_opt_nfw(theta, Rp, Roff)\n",
+    "\n",
+    "def integrand_tpl_opt_ein(Rp, z, theta, Roff):\n",
+    "    x = np.sqrt(z**2. + Rp**2. + Roff**2. - 2.*Rp*Roff*np.cos(theta)) / r_s\n",
+    "    return Rp * np.exp(-2. * (x**alpha_ein - 1.) / alpha_ein)\n",
+    "    #return Rp * integrand1_opt_ein(theta, Rp, Roff)\n",
+    "\n",
+    "def integrand_dbl_opt_ein(Rp, theta, Roff):\n",
+    "    return Rp * integrand1_opt_ein(theta, Rp, Roff)\n",
+    "\n",
+    "def integrand_dbl_opt_her(Rp, theta, Roff):\n",
+    "    return Rp * integrand1_opt_her(theta, Rp, Roff)\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_dbl(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.dblquad(integrand_dbl_opt, 0, np.pi, R_lower, R, args=[Roff], epsrel=1.e-6)[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2/np.pi\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_nfw_dbl(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.dblquad(integrand_dbl_opt_nfw, 0, np.pi, R_lower, R, args=[Roff])[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2/np.pi\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_ein_tpl(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.tplquad(integrand_tpl_opt_ein, 0, np.pi, 0, np.inf, R_lower, R, args=[Roff])[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2/np.pi * 2. * rho_s_ein\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_ein_dbl(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.dblquad(integrand_dbl_opt_ein, 0, np.pi, R_lower, R, args=[Roff])[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2/np.pi\n",
+    "    return res\n",
+    "\n",
+    "def Sigma_mean_mis_exact_opt_her_dbl(R_arr, Roff):\n",
+    "    res = np.zeros_like(R_arr)\n",
+    "    for i, R in enumerate(R_arr):\n",
+    "        R_lower = 0 if i==0 else R_arr[i-1]\n",
+    "        res[i] = integrate.dblquad(integrand_dbl_opt_her, 0, np.pi, R_lower, R, args=[Roff])[0]\n",
+    "    res = np.cumsum(res)*2/R_arr**2/np.pi\n",
+    "    return res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5928557a-495f-46a4-8f7a-24f8d52bd534",
+   "metadata": {},
+   "source": [
+    "## Dsig for all variants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8866063a-b39f-4b2d-b9f3-83309ce2045b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def DS_mis_approx(R_arr, Roff, regrid=1000):\n",
+    "    return Sigma_mean_mis_trap(R_arr, Roff, regrid=regrid) - Sigma_mis_exact(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact(R_arr, Roff) - Sigma_mis_exact(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt(R_arr, Roff) - Sigma_mis_exact_opt(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt_nfw(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_nfw(R_arr, Roff) - np.array([Sigma_mis_exact_opt_nfw(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_ein(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_ein(R_arr, Roff) - Sigma_mis_exact_opt_ein(R_arr, Roff)\n",
+    "\n",
+    "def DS_mis_exact_opt_her(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_her(R_arr, Roff) - np.array([Sigma_mis_exact_opt_her(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_dbl(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_dbl(R_arr, Roff) - np.array([Sigma_mis_exact_opt(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_nfw_dbl(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_nfw_dbl(R_arr, Roff) - np.array([Sigma_mis_exact_opt_nfw(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_ein_dbl(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_ein_dbl(R_arr, Roff) - np.array([Sigma_mis_exact_opt_ein(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_ein_tpl(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_ein_tpl(R_arr, Roff) - np.array([Sigma_mis_exact_opt_ein(R_, Roff) for R_ in R_arr])\n",
+    "\n",
+    "def DS_mis_exact_opt_her_dbl(R_arr, Roff):\n",
+    "    return Sigma_mean_mis_exact_opt_her_dbl(R_arr, Roff) - np.array([Sigma_mis_exact_opt_her(R_, Roff) for R_ in R_arr])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "867e519e-9db9-42d5-a267-3647a7d00120",
+   "metadata": {},
+   "source": [
+    "## Speed and precision tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b18da7b-9e45-4f49-a7ec-fc07cc335308",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff = 0.2\n",
+    "R_arr = np.logspace(-2, 1, 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd352829-787d-4d92-a795-70181384352d",
+   "metadata": {},
+   "source": [
+    "### Sigma opt with ccl backend, NFW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4abe8db2-7366-48b1-a0c1-8601375a4073",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 52 ms, sys: 10.1 ms, total: 62.1 ms\n",
+      "Wall time: 58.2 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "Sigma_mis = Sigma_mis_exact_opt(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79413921-095c-4b65-856c-2a44eac3170b",
+   "metadata": {},
+   "source": [
+    "### DeltaSigma for original implementation, ccl+NFW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b0c4855-a880-4f97-a381-ff2b9ff4875c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact = DS_mis_exact(R_arr, Roff)\n",
+    "# won't finish (or takes very long) for CCL Einasto"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67308412-48e7-4c8a-86d1-99d85ae8ea03",
+   "metadata": {},
+   "source": [
+    "### DeltaSigma optimized, ccl+NFW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "637072f5-b351-437b-be59-11b55e9b7998",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 36.6 s, sys: 702 ms, total: 37.3 s\n",
+      "Wall time: 37.8 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt = DS_mis_exact_opt(R_arr, Roff)\n",
+    "# won't finish (or takes very long) for CCL Einasto"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "565b6d1c-a9b4-49b1-805a-cba520bf5971",
+   "metadata": {},
+   "source": [
+    "Same but with doublequad for DeltaSigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4f04361b-6ebd-419a-a1a3-d0077b19f73c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.96 s, sys: 106 ms, total: 6.07 s\n",
+      "Wall time: 6.19 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_dbl = DS_mis_exact_opt_dbl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44360cf-ec81-4e2c-9632-7920ee67d7c6",
+   "metadata": {},
+   "source": [
+    "### Backend independant optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f38318b-c1d8-4901-9a53-b4982d4aa757",
+   "metadata": {},
+   "source": [
+    "For **NFW** profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b9464aa4-c381-495d-81da-8ecaede9c2b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.73 s, sys: 35.2 ms, total: 1.76 s\n",
+      "Wall time: 1.79 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_nfw = DS_mis_exact_opt_nfw(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2c24c5d-268d-4971-a140-9ac458f86561",
+   "metadata": {},
+   "source": [
+    "For **NFW** profile + **dblquad** for DeltaSigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a4075f76-aa6a-4fa0-8543-d53d6cb9b4b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 286 ms, sys: 7.02 ms, total: 293 ms\n",
+      "Wall time: 294 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_nfw_dbl = DS_mis_exact_opt_nfw_dbl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25ec6a2c-4e38-4c77-ae81-7d23344b27da",
+   "metadata": {},
+   "source": [
+    "For **Hernquist** profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1919d427-2c68-4a76-9702-bcedb141a228",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.69 s, sys: 36.3 ms, total: 1.72 s\n",
+      "Wall time: 1.72 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_her = DS_mis_exact_opt_her(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e64607b1-93d0-4556-b46a-5c6f40318933",
+   "metadata": {},
+   "source": [
+    "For **Hernquist** profile + **dblquad** for DeltaSigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "98d7968d-6d21-4f26-bfa3-e98e8e6d1a41",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 299 ms, sys: 7 ms, total: 306 ms\n",
+      "Wall time: 307 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_her_dbl = DS_mis_exact_opt_her_dbl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5945437d-7518-4588-beea-b53d5e2386f6",
+   "metadata": {},
+   "source": [
+    "For **Einasto** profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1471db64-f53b-4525-9b1d-f08f0fad8d5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 54s, sys: 4.08 s, total: 2min 58s\n",
+      "Wall time: 3min 2s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_ein = DS_mis_exact_opt_ein(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59e50583-26a0-459d-a221-710a545e940f",
+   "metadata": {},
+   "source": [
+    "For **Einasto** profile + **dblquad** for DeltaSigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9f34ee0a-246d-43fc-aec6-1f256eb24edc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 56.9 s, sys: 1.57 s, total: 58.5 s\n",
+      "Wall time: 59.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_ein_dbl = DS_mis_exact_opt_ein_dbl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "001ec48d-5787-49bc-9464-13c18587ab6f",
+   "metadata": {},
+   "source": [
+    "For **Einasto** profile + **tplquad** for DeltaSigma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a6c8225b-1df0-483d-9b22-9b3cdee2498c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 14.8 s, sys: 219 ms, total: 15.1 s\n",
+      "Wall time: 15.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_exact_opt_ein_tpl = DS_mis_exact_opt_ein_tpl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3564b1f-522e-4479-946c-d1fca3a4f719",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "730163ec-36f1-429a-9c49-6af4ba69ba88",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "97b7a007-9666-469c-ae55-cbd1872276ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 213 ms, sys: 15.6 ms, total: 229 ms\n",
+      "Wall time: 226 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e2 = DS_mis_approx(R_arr, Roff, regrid=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6f1212cd-b553-4324-aac1-a9a36d529c01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 263 ms, sys: 7.69 ms, total: 271 ms\n",
+      "Wall time: 273 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e3 = DS_mis_approx(R_arr, Roff, regrid=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "295d08ac-bf37-41d8-86f9-71afddbfc6a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 990 ms, sys: 55.2 ms, total: 1.05 s\n",
+      "Wall time: 1.05 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "DeltaSigma_mis_1e4 = DS_mis_approx(R_arr, Roff, regrid=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cedfbd48-bd30-47f0-b5cc-8210d8a9d4f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 55.2 s, sys: 932 ms, total: 56.1 s\n",
+      "Wall time: 56.5 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "Delta_Sigma_mis_ein_dbl = DS_mis_exact_opt_ein_dbl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "72a3c019-c680-4d88-9501-4077548dab53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 14.8 s, sys: 208 ms, total: 15 s\n",
+      "Wall time: 15.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "Delta_Sigma_mis_ein_tpl = DS_mis_exact_opt_ein_tpl(R_arr, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fe9a63aa-053f-47dd-a3dc-c08a3bd380c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1899354636080162e-06"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(Delta_Sigma_mis_ein_dbl/Delta_Sigma_mis_ein_tpl - 1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "904c10af-cdc0-43a9-adf5-6d1458f18d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3094301881523075"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(DeltaSigma_mis_exact_opt_ein/DeltaSigma_mis_dbl - 1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f77bdc69-0e3e-4586-8f95-7c3988b4fbe8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30943018815232526"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs(Delta_Sigma_mis_ein_dbl/DeltaSigma_mis_exact_opt-1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "733b8022-28c3-4569-8a43-3cfa9e354fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "79ad47b9-9465-4201-af23-8ca50cf2afa9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Sigma = moo.eval_surface_density(R_arr, z_cl)\n",
+    "DeltaSigma = moo.eval_excess_surface_density(R_arr, z_cl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9ff81ea0-ec8b-4b38-aa29-8c3ae6b27532",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(ncols=3, figsize=(13,4))\n",
+    "axes[0].loglog(R_arr, Sigma, label='No miscentering', marker='+')\n",
+    "axes[0].loglog(R_arr, Sigma_mis, label='Miscentered', marker='+')\n",
+    "axes[0].axvline(Roff, c='k', ls=':')\n",
+    "axes[0].set_xlabel('R[Mpc]')\n",
+    "axes[0].set_ylabel(r'$\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[1].loglog(R_arr, DeltaSigma, label='No miscentering', marker='+')\n",
+    "#axes[1].loglog(R_arr, DeltaSigma_mis_exact, label='Miscentering, exact', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_exact_opt, label='Miscentering, exact(opt)', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e2, label='Miscentered, 1e2', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e3, label='Miscentered, 1e3', marker='+')\n",
+    "axes[1].loglog(R_arr, DeltaSigma_mis_1e4, label='Miscentered, 1e4', marker='+')\n",
+    "#axes[1].loglog(R_arr, DeltaSigma_mis_1e5, label='Miscentered, 1e5', marker='+')\n",
+    "axes[1].axvline(Roff, c='k', ls=':')\n",
+    "axes[1].legend()\n",
+    "axes[1].set_xlabel('R[Mpc]')\n",
+    "axes[1].set_ylabel(r'$\\Delta\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e2/DeltaSigma_mis_exact_opt)-1), marker='+', label='approx, 1e2')\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e3/DeltaSigma_mis_exact_opt)-1), marker='+', label='approx, 1e3')\n",
+    "axes[2].loglog(R_arr, np.abs((DeltaSigma_mis_1e4/DeltaSigma_mis_exact_opt)-1), marker='+', label='approx, 1e4')\n",
+    "axes[2].axvline(Roff, c='k', ls=':')\n",
+    "axes[2].set_xlabel('R[Mpc]')\n",
+    "axes[2].set_ylabel(r'$|\\Delta\\Sigma^{\\rm approx}/\\Delta\\Sigma^{\\rm exact}|-1$')\n",
+    "\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "axes[2].legend()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "299ac651-3460-4238-af9e-634fd76b2823",
+   "metadata": {},
+   "source": [
+    "# Stacked profiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fdbc52b8-3b10-4d24-b730-b699718eb6fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c23c2f4f-cd2e-4592-9329-890be4fed4b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Roff_distrib(Roff, Rmis=0.2):\n",
+    "    return np.exp(-Roff/Rmis) * Roff/(Rmis*Rmis)\n",
+    "\n",
+    "def integrand_Sigmastack_mean_exact(Rprime, Roff): \n",
+    "    return Rprime * Sigma_stack_mis_exact(Rprime, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "445afbd4-f3e2-4c8b-83cf-22d753b322fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x12c463450>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Roff_arr = np.linspace(0, 20*Roff, 100)\n",
+    "plt.plot(Roff_arr, Roff_distrib(Roff_arr, Rmis=Roff)/Roff_distrib(Roff, Rmis=Roff))\n",
+    "plt.axvline(Roff, linestyle=':')\n",
+    "plt.axvline(10.*Roff, linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "57d36ce1-16d2-4771-a8b8-e2cbb2536993",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x12cf31350>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.loglog(Roff_arr, Roff_distrib(Roff_arr, Rmis=Roff)/Roff_distrib(Roff, Rmis=Roff))\n",
+    "plt.axvline(Roff, linestyle=':')\n",
+    "plt.axvline(10.*Roff, linestyle=':')\n",
+    "plt.axhline(1.e-5, linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4fe426b5-e470-4506-993c-74bb5c4a8b1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R_arr_int = np.logspace(-5, 4, 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc00206-63f5-48ef-bc07-c05a2a82906e",
+   "metadata": {},
+   "source": [
+    "## Original implementation \n",
+    "Exact calculation for Sigma + simpson integration for convolution with Roff distribution + approx trapezoid integration for Sigma mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3b8c0083-0b0f-4855-bc03-c4ebc7f0ea98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Sigma_mis_interp(R, Roff):\n",
+    "    Sigma_mis = Sigma_mis_exact(R_arr_int, Roff)\n",
+    "    f_sigmamis = interp1d(R_arr_int, Sigma_mis)\n",
+    "    #print(Sigma_mis)\n",
+    "    #print(f_sigmamis(R))\n",
+    "    return f_sigmamis(R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d68dd99-f655-4d78-8c9b-252bd8d1039c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "861932f0-1c23-4efa-ba5b-4cd039160490",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand_stack(Roff, R, Rmis):\n",
+    "    return Sigma_mis_interp(R, Roff) * Roff_distrib(Roff, Rmis)\n",
+    "\n",
+    "def integrand_stack_tab(Roff_tab, R, Rmis):\n",
+    "    return np.array([Sigma_mis_interp(R, r) * Roff_distrib(r, Rmis) for r in Roff_tab])\n",
+    "\n",
+    "def Sigma_stack_mis_exact(R_arr, Rmis=3.):\n",
+    "    return integrate.quad_vec(integrand_stack, 1.e-5, 1.e5, args=(R, Rmis))[0]\n",
+    "\n",
+    "def Sigma_stack_mis_simps(R_arr, Rmis, Rinfty_scale=10, ngrid=100):\n",
+    "    Roff_tab = np.linspace(0.,Rmis*Rinfty_scale,ngrid)\n",
+    "    tab = integrand_stack_tab(Roff_tab, R_arr, Rmis)\n",
+    "    return integrate.simpson(tab, x=Roff_tab, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "f0706cf4-12c5-4595-8613-5b766e6804ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.19 s, sys: 272 ms, total: 9.46 s\n",
+      "Wall time: 10.3 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "Sigma_mis_stack_simps = Sigma_stack_mis_simps(R_arr, Rmis=Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb86d76-dfd6-4b76-b275-63fd36143be8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "d24d87e6-3a63-4769-b6e9-16c0484d07e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def Sigma_mean_mis_stack_trap(R_arr, Rmis, ngrid=100, Rinfty_scale=10, ngrid_sigma=100):\n",
+    "\n",
+    "    new_R_arr = np.logspace(np.log10(1.e-5), np.log10(R_arr.max()), ngrid)\n",
+    "    res = (2./new_R_arr**2) * integrate.cumulative_trapezoid(new_R_arr * Sigma_stack_mis_simps(new_R_arr, Rmis, Rinfty_scale=Rinfty_scale, ngrid=ngrid_sigma), new_R_arr, initial=0)\n",
+    "\n",
+    "    f = interp1d(new_R_arr, res)\n",
+    "    return f(R_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "bce134c5-65dd-4655-a2ef-48c44e68cbf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff=0.2\n",
+    "R_arr2 = np.logspace(-2, np.log10(20), 50)\n",
+    "\n",
+    "Sigma = moo.eval_surface_density(R_arr2, z_cl)\n",
+    "Sigma_mis = Sigma_mis_exact(R_arr2, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "0339d067-64c6-4973-bb0e-c15efa2c9afb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DeltaSigma = moo.eval_excess_surface_density(R_arr2, z_cl)\n",
+    "DeltaSigma_mis = DS_mis_approx(R_arr2, Roff, regrid=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c6198214-f7c5-42cc-8f36-8856f0368c75",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 52s, sys: 4.23 s, total: 2min 57s\n",
+      "Wall time: 3min 3s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "Sigma_mis_stack = Sigma_stack_mis_simps(R_arr2, Rmis=Roff, Rinfty_scale=10, ngrid=1000)\n",
+    "Sigma_mean_stack_mis_trap = Sigma_mean_mis_stack_trap(R_arr2, Rmis=Roff, ngrid=1000, Rinfty_scale=10, ngrid_sigma=1000)\n",
+    "DeltaSigma_stack_mis = Sigma_mean_stack_mis_trap - Sigma_mis_stack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "63177a36-bbd0-4e6a-8a43-f4e7f71950ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tst\n",
+    "\n",
+    "def DS_mis_interp(R,Roff,ngrid):\n",
+    "    new_R_arr = np.logspace(np.log10(1.e-5), np.log10(R_arr.max()), ngrid)\n",
+    "\n",
+    "    ## Sigma\n",
+    "    Sigma_mis = Sigma_mis_exact(new_R_arr, Roff)\n",
+    "    f_sigma = interp1d(new_R_arr, Sigma_mis)\n",
+    "\n",
+    "    ## Sigma mean\n",
+    "    Sigmamean_mis = (2./new_R_arr**2) * integrate.cumulative_trapezoid(new_R_arr * f_sigma(new_R_arr), new_R_arr, initial=0)\n",
+    "    f_sigmamean = interp1d(new_R_arr, Sigmamean_mis)\n",
+    "\n",
+    "    return f_sigmamean(R_arr) - f_sigma(R_arr)\n",
+    "\n",
+    "\n",
+    "def DS_integrand_stack_tab(Roff_tab, R, Rmis, ngrid):\n",
+    "    return np.array([DS_mis_interp(R,r, ngrid) * Roff_distrib(r, Rmis) for r in Roff_tab])\n",
+    "\n",
+    "\n",
+    "def DS_stack_mis_tst(R_arr, Rmis, Rinfty_scale=10, ngrid=1000):\n",
+    "    Roff_tab = np.linspace(0.,Rmis*Rinfty_scale,ngrid)\n",
+    "    tab = DS_integrand_stack_tab(Roff_tab, R_arr, Rmis, ngrid)\n",
+    "    #tab = np.array([DS_mis_interp(R_arr, r, ngrid) * Roff_distrib(r, Rmis) for r in Roff_tab])\n",
+    "    return integrate.simpson(tab, x=Roff_tab, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0f70750-3525-4853-b579-d402c490ee2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "Roff_tab = np.linspace(0.,6, 1000)\n",
+    "tab = DS_integrand_stack_tab(Roff_tab, R_arr2, Roff, 1000)\n",
+    "#DeltaSigma_stack_mis_tst = DS_stack_mis_tst(R_arr2, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10d83dd3-602b-481e-8eda-d9c844bef3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "220a70fa-87c7-49b5-85b0-7ed3d01d5ba1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c9c47ba-dfee-478e-9d63-9b1ce0fa89ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(ncols=2, figsize=(10, 4))\n",
+    "axes[0].loglog(R_arr2, Sigma, label='No miscentering', marker='+')\n",
+    "axes[0].loglog(R_arr2, Sigma_mis, label='Miscentered, single', marker='+')\n",
+    "axes[0].loglog(R_arr2, Sigma_mis_stack, label='Miscentered, stack', marker='+')\n",
+    "axes[0].axvline(Roff, c='k', ls=':')\n",
+    "axes[0].set_xlabel('R[Mpc]')\n",
+    "axes[0].set_ylabel(r'$\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[1].loglog(R_arr2, DeltaSigma, label='No miscentering', marker='+')\n",
+    "axes[1].loglog(R_arr2, DeltaSigma_mis, label='Miscentered, single, 1e3', marker='+')\n",
+    "axes[1].loglog(R_arr2, DeltaSigma_stack_mis, label='Miscentered, stack, 1.e3', marker='+')\n",
+    "axes[1].loglog(R_arr2, DeltaSigma_stack_mis_opt_1000, label='Miscentered, stack, opt', marker='+')\n",
+    "axes[1].axvline(Roff, c='k', ls=':')\n",
+    "axes[1].legend()\n",
+    "axes[1].set_xlabel('R[Mpc]')\n",
+    "axes[1].set_ylabel(r'$\\Delta\\Sigma$ [M$_\\odot$ Mpc$^{-2}$]')\n",
+    "\n",
+    "axes[0].legend()\n",
+    "axes[1].legend()\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adb78327-847b-4cef-8b69-f7858cf987a7",
+   "metadata": {},
+   "source": [
+    "## With optimized Dsig + convolution (in prog)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe51e1c8-a6c0-4806-a4ca-fdcad2984a77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand_DS_stack(Roff, R_arr, Rmis):\n",
+    "    return DS_mis_exact_opt_dbl(R_arr, Roff) * Roff_distrib(Roff, Rmis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "767010c4-e423-407f-ae2c-651001b50b81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def DS_stack_opt_exact(R_arr, Rmis, Rinfty_scale=10):\n",
+    "    return integrate.quad_vec(integrand_DS_stack, 0., Rmis*Rinfty_scale, args=(R_arr, Rmis))[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69a6596d-ace7-4a2c-b4ad-400db663bf7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "#DeltaSigma_stack_mis_opt_exact = DS_stack_opt_exact(R_arr2, Roff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce44e660-090f-41cf-b06f-d6a963bae373",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def integrand_DS_stack_tab(Roff_tab, R, Rmis):\n",
+    "    return np.array([DS_mis_exact_opt(R, r) * Roff_distrib(r, Rmis) for r in Roff_tab])\n",
+    "\n",
+    "def DS_stack_opt_tab(R_arr, Rmis, Rinfty_scale=10, ngrid=1000):\n",
+    "    Roff_tab = np.linspace(0.,Rmis*Rinfty_scale,ngrid)\n",
+    "    #Roff_tab = np.logspace(-2,np.log(Rmis*Rinfty_scale),ngrid)\n",
+    "    tab = integrand_DS_stack_tab(Roff_tab, R_arr, Rmis)\n",
+    "    return integrate.simpson(tab, Roff_tab, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c62ca0b-01e9-4a33-b3ff-99c70714db3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%%time\n",
+    "#DeltaSigma_stack_mis_opt_20 = DS_stack_opt_tab(R_arr2, Roff, Rinfty_scale=10, ngrid=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f5aaaad-442e-45b1-9cb3-a34b8c354ed0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DeltaSigma_stack_mis_opt_10 =  DeltaSigma_stack_mis_opt_1000.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5893fbca-652a-4e0b-a7c6-60c893953691",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Roff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c001b9a-118c-429d-90a1-9be0b686bced",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/test_theory.py b/tests/test_theory.py
index 6a5a7f96d..d93c39f18 100644
--- a/tests/test_theory.py
+++ b/tests/test_theory.py
@@ -341,6 +341,75 @@ def test_profiles(modeling_data, profile_init):
             cfg["numcosmo_profiles"]["DeltaSigma"],
             reltol,
         )
+        # Test miscentering
+        if mod.backend == "nc":
+            assert_allclose(
+                mod.eval_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                )[-40:],
+                cfg["numcosmo_profiles"]["Sigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                mod.eval_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                    mis_from_backend=True,
+                )[-40:],
+                cfg["numcosmo_profiles"]["Sigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                mod.eval_mean_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                )[-40:],
+                (cfg["numcosmo_profiles"]["Sigma"] + cfg["numcosmo_profiles"]["DeltaSigma"])[-40:],
+                8.5e-3,
+            )
+            assert_allclose(
+                mod.eval_mean_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                    mis_from_backend=True,
+                )[-40:],
+                (cfg["numcosmo_profiles"]["Sigma"] + cfg["numcosmo_profiles"]["DeltaSigma"])[-40:],
+                8.5e-3,
+            )
+            assert_allclose(
+                mod.eval_excess_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                )[-40:],
+                cfg["numcosmo_profiles"]["DeltaSigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                mod.eval_excess_surface_density(
+                    cfg["SIGMA_PARAMS"]["r_proj"],
+                    cfg["SIGMA_PARAMS"]["z_cl"],
+                    r_mis=0.1,
+                    verbose=True,
+                    mis_from_backend=False,
+                )[-40:],
+                cfg["numcosmo_profiles"]["DeltaSigma"][-40:],
+                2.5e-2,
+            )
+        assert_equal(theo.miscentering.integrand_surface_density_nfw(0, 0.3, 0, 0.3), 1.0 / 3.0)
+        assert_equal(
+            theo.miscentering.integrand_surface_density_hernquist(0, 0.3, 0, 0.3), 4.0 / 15.0
+        )
         if mod.backend == "ct":
             assert_raises(
                 ValueError, mod.eval_excess_surface_density, 1e-12, cfg["SIGMA_PARAMS"]["z_cl"]
@@ -377,6 +446,66 @@ def test_profiles(modeling_data, profile_init):
             reltol,
         )
 
+        # Test miscentering
+        if mod.backend == "nc":
+            assert_allclose(
+                theo.compute_surface_density(
+                    cosmo=cosmo, **cfg["SIGMA_PARAMS"], alpha_ein=alpha_ein, verbose=True, r_mis=0.1
+                )[-40:],
+                cfg["numcosmo_profiles"]["Sigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                theo.compute_surface_density(
+                    cosmo=cosmo,
+                    **cfg["SIGMA_PARAMS"],
+                    alpha_ein=alpha_ein,
+                    verbose=True,
+                    r_mis=0.1,
+                    mis_from_backend=True,
+                )[-40:],
+                cfg["numcosmo_profiles"]["Sigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                theo.compute_mean_surface_density(
+                    cosmo=cosmo, **cfg["SIGMA_PARAMS"], alpha_ein=alpha_ein, verbose=True, r_mis=0.1
+                )[-40:],
+                (cfg["numcosmo_profiles"]["Sigma"] + cfg["numcosmo_profiles"]["DeltaSigma"])[-40:],
+                8.5e-3,
+            )
+            assert_allclose(
+                theo.compute_mean_surface_density(
+                    cosmo=cosmo,
+                    **cfg["SIGMA_PARAMS"],
+                    alpha_ein=alpha_ein,
+                    verbose=True,
+                    r_mis=0.1,
+                    mis_from_backend=True,
+                )[-40:],
+                (cfg["numcosmo_profiles"]["Sigma"] + cfg["numcosmo_profiles"]["DeltaSigma"])[-40:],
+                8.5e-3,
+            )
+            assert_allclose(
+                theo.compute_excess_surface_density(
+                    cosmo=cosmo, **cfg["SIGMA_PARAMS"], alpha_ein=alpha_ein, verbose=True, r_mis=0.1
+                )[-40:],
+                cfg["numcosmo_profiles"]["DeltaSigma"][-40:],
+                2.5e-2,
+            )
+            assert_allclose(
+                theo.compute_excess_surface_density(
+                    cosmo=cosmo,
+                    **cfg["SIGMA_PARAMS"],
+                    alpha_ein=alpha_ein,
+                    verbose=True,
+                    r_mis=0.1,
+                    mis_from_backend=True,
+                )[-40:],
+                cfg["numcosmo_profiles"]["DeltaSigma"][-40:],
+                2.5e-2,
+            )
+
         # Test use_projected_quad
         if mod.backend == "ccl" and profile_init == "einasto":
             if hasattr(mod.hdpm, "projected_quad"):