VItamin-and-Bilby-with-Different-Noises.md

@@ -85,19 +85,19 @@ It can be seen that the consistency of results is good.
 Therefore, two tutorial scripts(4 and 15 parameters) can be accepted that they ran well on my laptop.      
 Then, in response to the suspicion that there may be errors in my previous scripts (I may add or delete some lines by mistake), I list the differences between the 15-parameter script and mine:          
 (i) Compared to the tutorial, my script misses the line:     
-'''     
+```
 bilby.core.utils.setup_logger(outdir=outdir, label=label)     
-'''     
+```
 However this doesn't matter to the results.     
 (ii) Priors are used differently. Priors used by tutorial are:(only different parts are shown)     
-'''     
+```
 priors['geocent_time'] = bilby.core.prior.Uniform(
     minimum=ref_geocent_time - 0.1,
     maximum=ref_geocent_time + 0.1,
     name='geocent_time', latex_label='$t_c$', unit='$s$') 
-'''     
+```
 Priors used by me are:     
-'''       
+```
 priors['luminosity_distance'] = bilby.core.prior.Uniform(name='luminosity_distance', minimum=1000, maximum=5000, unit='$Mpc_{\\odot}$')     
 priors['dec'] = bilby.core.prior.Cosine(name='dec')     
 priors['tilt_1'] = bilby.core.prior.Sine(name='tilt_1')      
@@ -107,22 +107,22 @@ priors['geocent_time'] = bilby.core.prior.Uniform(
     minimum=ref_geocent_time - 1,
     maximum=ref_geocent_time + 1,
     name='geocent_time', latex_label='$t_c$', unit='$s$')     
-'''        
+```
 (iii) Result function.    
 Tutorial:    
-'''     
+```
 result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', npoints=2500,
     outdir=outdir,
     label=label, maxmcmc=5000,
     conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)     
-'''    
+```
 Mine:    
-'''     
+```
 result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
      outdir=outdir, label=label)     
-'''     
+```
 ## (3) 15 parameters with (m1,m2) instead of (chirp mass, mass ratio)    
 We get results with good consistency in the sections above. This is to test if (m1,m2) will decrease the convergence.    
 ### (i) posterior    
@@ -138,9 +138,9 @@ JSD = 0.0459
 #### (a) distance     
 JSD = 0.0336       
 ![15d](uploads/0e97da3389404c5d4a673e4fb1423e26/15d.png)     
-It can be seen that the consistency of results is good.         
+It can be seen that the consistency of results is still good. Hence we decide to next test how BH encounter performs in the PE run.            
     
-# BH encounter    
+# 4. BH encounter    
 ## (1) BH enoucnter with marginalization(distance, time, phase)     
 ### (i) posterior     
 ![encounter](uploads/3681c0bbd9a90c7f5c9684e5ec5a891f/encounter.png)