__author__ = 'dgilman'
import numpy as np
from lenstronomy.LensModel.Profiles.base_profile import LensProfileBase
__all__ = ['CoreBurkert']
[docs]class CoreBurkert(LensProfileBase):
"""
lensing properties of a modified Burkert profile with variable core size
normalized by rho0, the central core density
"""
param_names = ['Rs', 'alpha_Rs', 'r_core', 'center_x', 'center_y']
lower_limit_default = {'Rs': 1, 'alpha_Rs': 0, 'r_core': 0.5, 'center_x': -100, 'center_y': -100}
upper_limit_default = {'Rs': 100, 'alpha_Rs': 100, 'r_core': 50, 'center_x': 100, 'center_y': 100}
[docs] def function(self, x, y, Rs, alpha_Rs, r_core, center_x=0, center_y=0):
"""
:param x: angular position
:param y: angular position
:param Rs: angular turn over point
:param alpha_Rs: deflection angle at Rs
:param center_x: center of halo
:param center_y: center of halo
:return:
"""
rho0 = self._alpha2rho0(alpha_Rs=alpha_Rs, Rs=Rs, r_core=r_core)
if Rs < 0.0000001:
Rs = 0.0000001
x_ = x - center_x
y_ = y - center_y
R = np.sqrt(x_ ** 2 + y_ ** 2)
f_ = self.cBurkPot(R, Rs, rho0, r_core)
return f_
[docs] def derivatives(self, x, y, Rs, alpha_Rs, r_core, center_x=0, center_y=0):
"""
deflection angles
:param x: x coordinate
:param y: y coordinate
:param Rs: scale radius
:param alpha_Rs: deflection angle at Rs
:param r_core: core radius
:param center_x:
:param center_y:
:return:
"""
rho0 = self._alpha2rho0(alpha_Rs=alpha_Rs, Rs=Rs, r_core=r_core)
if Rs < 0.0000001:
Rs = 0.0000001
x_ = x - center_x
y_ = y - center_y
R = np.sqrt(x_ ** 2 + y_ ** 2)
dx, dy = self.coreBurkAlpha(R, Rs, rho0, r_core, x_, y_)
return dx, dy
[docs] def hessian(self, x, y, Rs, alpha_Rs, r_core, center_x=0, center_y=0):
"""
:param x: x coordinate
:param y: y coordinate
:param Rs: scale radius
:param alpha_Rs: deflection angle at Rs
:param r_core: core radius
:param center_x:
:param center_y:
:return:
"""
if Rs < 0.0001:
Rs = 0.0001
x_ = x - center_x
y_ = y - center_y
R = np.sqrt(x_ ** 2 + y_ ** 2)
rho0 = self._alpha2rho0(alpha_Rs=alpha_Rs, Rs=Rs, r_core=r_core)
kappa = self.density_2d(x_, y_, Rs, rho0, r_core)
gamma1, gamma2 = self.cBurkGamma(R, Rs, rho0, r_core, x_, y_)
f_xx = kappa + gamma1
f_yy = kappa - gamma1
f_xy = gamma2
return f_xx, f_xy, f_xy, f_yy
[docs] def mass_2d(self, R, Rs, rho0, r_core):
"""
analytic solution of the projection integral
(convergence)
:param R: projected distance
:param Rs: scale radius
:param rho0: central core density
:param r_core: core radius
"""
x = R * Rs ** -1
p = Rs * r_core ** -1
gx = self._G(x, p)
m_2d = 2 * np.pi * rho0 * Rs ** 3 * gx
return m_2d
[docs] def coreBurkAlpha(self, R, Rs, rho0, r_core, ax_x, ax_y):
"""
deflection angle
:param R:
:param Rs:
:param rho0:
:param r_core:
:param ax_x:
:param ax_y:
:return:
"""
x = R * Rs ** -1
p = Rs * r_core ** -1
gx = self._G(x, p)
a = 2 * rho0 * Rs ** 2 * gx / x
return a * ax_x / R, a * ax_y / R
[docs] def density(self, R, Rs, rho0, r_core):
"""
three dimensional cored Burkert profile
:param R: radius of interest
:type R: float/numpy array
:param Rs: scale radius
:type Rs: float
:param rho0: characteristic density
:type rho0: float
:return: rho(R) density
"""
M0 = 4*np.pi * Rs ** 3 * rho0
return (M0 / (4*np.pi)) * ((r_core + R) * (Rs ** 2 + R ** 2)) ** -1
[docs] def density_2d(self, x, y, Rs, rho0, r_core, center_x=0, center_y=0):
"""
projected two dimenstional core Burkert profile (kappa*Sigma_crit)
:param x: x coordinate
:param y: y coordinate
:param Rs: scale radius
:param rho0: central core density
:param r_core: core radius
"""
x_ = x - center_x
y_ = y - center_y
R = np.sqrt(x_ ** 2 + y_ ** 2)
x = R * Rs ** -1
p = Rs * r_core ** -1
Fx = self._F(x, p)
return 2 * rho0 * Rs * Fx
[docs] def mass_3d(self, R, Rs, rho0, r_core):
"""
:param R: projected distance
:param Rs: scale radius
:param rho0: central core density
:param r_core: core radius
"""
Rs = float(Rs)
b = r_core * Rs ** -1
c = R * Rs ** -1
M0 = 4*np.pi*Rs**3 * rho0
return M0 * (1+b**2) ** -1 * (0.5*np.log(1+c**2) + b**2*np.log(c*b**-1 + 1) - b*np.arctan(c))
[docs] def cBurkPot(self, R, Rs, rho0, r_core):
"""
:param R: projected distance
:param Rs: scale radius
:param rho0: central core density
:param r_core: core radius
"""
x = R * Rs ** -1
p = Rs * r_core ** -1
hx = self._H(x, p)
return 2 * rho0 * Rs ** 3 * hx
[docs] def cBurkGamma(self, R, Rs, rho0, r_core, ax_x, ax_y):
"""
:param R: projected distance
:param Rs: scale radius
:param rho0: central core density
:param r_core: core radius
:param ax_x: x coordinate
:param ax_y: y coordinate
:return:
"""
c = 0.000001
if isinstance(R, int) or isinstance(R, float):
R = max(R, c)
else:
R[R <= c] = c
x = R * Rs ** -1
p = Rs * r_core ** -1
gx = self._G(x, p)
fx = self._F(x, p)
m_x = 2 * rho0 * Rs ** 3 * gx
kappa = 2 * rho0 * Rs * fx
a = 2 * (m_x * R ** -2 - kappa)
return 0.5 * a * (ax_y ** 2 - ax_x ** 2) / R ** 2, -a * (ax_x * ax_y) / R ** 2
@staticmethod
def _u(x):
return np.sqrt(1 + x ** 2)
@staticmethod
def _g(x, p):
return np.sqrt(1 - x ** 2 * p ** 2)
@staticmethod
def _f(x, p):
return np.sqrt(x ** 2 * p ** 2 - 1)
def _H(self, x, p):
prefactor = (p + p ** 3) ** -1 * p
if isinstance(x, np.ndarray):
inds0 = np.where(x * p == 1)
inds1 = np.where(x * p < 1)
inds2 = np.where(x * p > 1)
func = np.ones_like(x)
func[inds1] = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x[inds1]) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x[inds1]) - 0.5 * np.arctanh(self._u(x[inds1]) ** -1)) * np.arctanh(
self._u(x[inds1]) ** -1) + \
2 * (self._g(x[inds1], p) - 0.5 * np.arctanh(self._g(x[inds1], p))) * \
np.arctanh(self._g(x[inds1], p)) + (1 + p ** 2) * np.log(x[inds1]) ** 2 - np.pi * p * \
np.log(1 + self._u(x[inds1])) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x[inds1]) * (
0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
func[inds2] = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x[inds2]) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x[inds2]) - 0.5 * np.arctanh(self._u(x[inds2]) ** -1)) * np.arctanh(
self._u(x[inds2]) ** -1) + \
-2 * (self._f(x[inds2], p) - 0.5 * np.arctan(self._f(x[inds2], p))) * \
np.arctan(self._f(x[inds2], p)) + (1 + p ** 2) * np.log(x[inds2]) ** 2 - np.pi * p * \
np.log(1 + self._u(x[inds2])) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x[inds2]) * (
0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
func[inds0] = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x[inds0]) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x[inds0]) - 0.5 * np.arctanh(self._u(x[inds0]) ** -1)) * np.arctanh(
self._u(x[inds0]) ** -1) \
+ (1 + p ** 2) * np.log(x[inds0]) ** 2 - np.pi * p * \
np.log(1 + self._u(x[inds0])) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x[inds0]) * (
0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
else:
if x * p < 1:
func = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x) - 0.5 * np.arctanh(self._u(x) ** -1)) * np.arctanh(self._u(x) ** -1) + \
2 * (self._g(x, p) - 0.5 * np.arctanh(self._g(x, p))) * \
np.arctanh(self._g(x, p)) + (1 + p ** 2) * np.log(x) ** 2 - np.pi * p * \
np.log(1 + self._u(x)) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x) * (0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
elif x * p > 1:
func = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x) - 0.5 * np.arctanh(self._u(x) ** -1)) * np.arctanh(
self._u(x) ** -1) + \
-2 * (self._f(x, p) - 0.5 * np.arctan(self._f(x, p))) * \
np.arctan(self._f(x, p)) + (1 + p ** 2) * np.log(x) ** 2 - np.pi * p * \
np.log(1 + self._u(x)) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x) * (
0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
else:
func = 0.9058413472016891 + (-0.9640065632861909 + np.pi * self._u(x) -
0.9058413472016892 * p) * p + 2 * p ** 2 * (
self._u(x) - 0.5 * np.arctanh(self._u(x) ** -1)) * np.arctanh(self._u(x) ** -1) \
+ (1 + p ** 2) * np.log(x) ** 2 - np.pi * p * \
np.log(1 + self._u(x)) + (0.3068528194400547 + 0.25 * np.log(p ** 2)) * \
np.log(p ** 2) + np.log(x) * (0.6137056388801094 + 0.6137056388801094 * p ** 2 + np.log(p ** 2))
return prefactor * func
def _F(self, x, p):
"""
solution of the projection integal (kappa)
arctanh / arctan function
:param x: r/Rs
:param p: r_core / Rs
:return:
"""
prefactor = 0.5 * (1 + p ** 2) ** -1 * p
if isinstance(x, np.ndarray):
inds0 = np.where(x * p == 1)
inds1 = np.where(x * p < 1)
inds2 = np.where(x * p > 1)
func = np.ones_like(x)
func[inds0] = self._u(x[inds0]) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x[inds0]) ** -1))
func[inds1] = self._u(x[inds1]) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x[inds1]) ** -1)) - \
(2 * p * self._g(x[inds1], p) ** -1 * np.arctanh(self._g(x[inds1], p)))
func[inds2] = self._u(x[inds2]) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x[inds2]) ** -1)) - \
(2 * p * self._f(x[inds2], p) ** -1 * np.arctan(self._f(x[inds2], p)))
return prefactor * func
else:
if x * p == 1:
func = self._u(x) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x) ** -1))
elif x * p < 1:
func = self._u(x) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x) ** -1)) - \
(2 * p * self._g(x, p) ** -1 * np.arctanh(self._g(x, p)))
else:
func = self._u(x) ** -1 * (np.pi + 2 * p * np.arctanh(self._u(x) ** -1)) - \
(2 * p * self._f(x, p) ** -1 * np.arctan(self._f(x, p)))
return prefactor * func
def _G(self, x, p):
"""
analytic solution of the 2d projected mass integral
integral: 2 * pi * x * kappa * dx
:param x:
:param p:
:return:
"""
prefactor = (p + p ** 3) ** -1 * p
if isinstance(x, np.ndarray):
inds0 = np.where(x * p == 1)
inds1 = np.where(x * p < 1)
inds2 = np.where(x * p > 1)
func = np.ones_like(x)
func[inds0] = np.log(0.25 * x[inds0] ** 2 * p ** 2) + np.pi * p * (self._u(x[inds0]) - 1) + \
2 * p ** 2 * (self._u(x[inds0]) * np.arctanh(self._u(x[inds0]) ** -1) +
np.log(0.5 * x[inds0]))
func[inds1] = np.log(0.25 * x[inds1] ** 2 * p ** 2) + np.pi * p * (self._u(x[inds1]) - 1) + \
2 * p ** 2 * (self._u(x[inds1]) * np.arctanh(self._u(x[inds1]) ** -1) +
np.log(0.5 * x[inds1])) + 2 * self._g(x[inds1], p) * np.arctanh(
self._g(x[inds1], p))
func[inds2] = np.log(0.25 * x[inds2] ** 2 * p ** 2) + np.pi * p * (self._u(x[inds2]) - 1) + \
2 * p ** 2 * (self._u(x[inds2]) * np.arctanh(self._u(x[inds2]) ** -1) +
np.log(0.5 * x[inds2])) - 2 * self._f(x[inds2], p) * np.arctan(
self._f(x[inds2], p))
else:
if x * p == 1:
func = np.log(0.25 * x ** 2 * p ** 2) + np.pi * p * (self._u(x) - 1) + \
2 * p ** 2 * (self._u(x) * np.arctanh(self._u(x) ** -1) +
np.log(0.5 * x))
elif x * p < 1:
func = np.log(0.25 * x ** 2 * p ** 2) + np.pi * p * (self._u(x) - 1) + \
2 * p ** 2 * (self._u(x) * np.arctanh(self._u(x) ** -1) +
np.log(0.5 * x)) + 2 * self._g(x, p) * np.arctanh(self._g(x, p))
else:
func = np.log(0.25 * x ** 2 * p ** 2) + np.pi * p * (self._u(x) - 1) + \
2 * p ** 2 * (self._u(x) * np.arctanh(self._u(x) ** -1) +
np.log(0.5 * x)) - 2 * self._f(x, p) * np.arctan(self._f(x, p))
return func * prefactor
def _alpha2rho0(self, alpha_Rs=None, Rs=None, r_core=None):
p = Rs * r_core ** -1
gx = self._G(1, p)
rho0 = alpha_Rs / (2 * Rs ** 2 * gx)
return rho0
def _rho2alpha(self, rho0=None, Rs=None, r_core=None):
p = Rs / r_core
gx = self._G(1, p)
alpha = 2 * Rs ** 2 * gx * rho0
return alpha