SN likelihood corrected

Author: Sandeepharidasu

Notebooks/likelihood.ipynb

@@ -38,7 +38,7 @@ def sound_horizon(H0,Omega_b,Omega_m,Obsample):
 
 @njit([f8[:](f8[:])])
 def z_inp(z):
-    return np.arange(0.,np.max(z)+.5,0.01)
+    return np.arange(0.,np.max(z)+.5,0.001)
 
 @njit
 def interpolate(z_inp,z,func):
@@ -55,4 +55,4 @@ def transverse_distance(H0,Omega_m,Omega_b,Omega_k,de,z,clight):
 
 @njit
 def distance_modulus(Mb,z2,d):
-    return Mb + 25. + 5.*np.log10( (1+ z2)* d )
+    return Mb + 25. + 5.*np.log10( (1+ z2) * d )

marcia/backend/cosmology.py

@@ -128,7 +128,7 @@ def distance_modulus(self,parameters,z1, z2):
             mu = cbackend.distance_modulus(Mb,z2,d)
         else:
             # This is useful if only SN data is needed to be used
-            Mb = -19.05
+            Mb = -19.2
             d = self.transverse_distance(parameters, z1)
             mu = cbackend.distance_modulus(Mb,z2,d)
         return mu

marcia/cosmology.py

@@ -50,7 +50,7 @@ def chisq_BAO_alam(self,theta):
     def chisq_Pantheon_plus(self,theta):
         cmb_z, mb, covariance = self.db.get_pantheon_plus()
         helio_z = self.db.get_pantheon_plus(Zhel=True)
-        distance_theory = self.theory.distance_modulus(theta, cmb_z,helio_z)
+        distance_theory = self.theory.distance_modulus(theta, cmb_z, helio_z)
         delta = mb - distance_theory
         if 'Pantheon_plus' in self.inv_covariance.keys():
             icov = self.inv_covariance['Pantheon_plus']

marcia/likelihood.py

@@ -18,8 +18,8 @@ fc = '$f_c$'
 [Priors]
 r_d = [100.0,180.0]
 M_b = [-20.0,-18.0]
-Omega_b = [0.01,0.05]
-H0 = [61.0,76.0]
+Omega_b = [0.01,0.1]
+H0 = [61.0,81.0]
 Omega_m = [0.1,0.5]
 w = [-2.5,0.5]
 Omega_k = [-0.7,0.6]

marcia/params.ini

@@ -28,7 +28,7 @@ def __init__(self,model,parameters,data,initial_guess,prior_file=None,max_n=1000
 
 
     def MLE(self,verbose=True):
-        nll = lambda x: -self.likelihood.logProb(x)
+        nll = lambda x: -1*self.likelihood.logProb(x)
         result = op.minimize(nll, x0=self.initial_guess, method = 'Nelder-Mead', options={'maxfev': None})
         if verbose:
             print(f'Best-fit values: {result.x}')