Following the tutorial source described in the tutorial resources of GWOSC, I tried to work on the parameter estimation (PE) of GW170817. Since GW170817 is the merger events of NSs, I changed all the packages from BHs to NSs (e.g., convert_to_lal_binary_neutron_star_parameters, generate_all_bns_parameters) The inspiral phase for NS merger is much longer than BH merger, so I considered to take the 16-s data for my test. And I determine the post_trigger_duration = 8 (sec). To generate the PSD, I still consider 128-s duration taken before the start of the analysis. In my preliminary test, I also used the ‘IMRPhenomPv2’ waveform and initialize the following parameters as the prior accord to the reference of some papers.

```
prior = bilby.core.prior.PriorDict()
prior['chirp_mass'] = Uniform(name='chirp_mass', minimum=1.0,maximum=1.5)
prior['mass_ratio'] = Uniform(name='mass_ratio', minimum=0.5, maximum=1)
prior['phase'] = Uniform(name="phase", minimum=0, maximum=2*np.pi)
prior['geocent_time'] = Uniform(name="geocent_time", minimum=time_of_event-0.1, maximum=time_of_event+0.1)
prior['a_1'] = 0.0
prior['a_2'] = 0.0
prior['tilt_1'] = 0.0
prior['tilt_2'] = 0.0
prior['phi_12'] = 0.0
prior['phi_jl'] = 0.0
prior['dec'] = -0.40808
prior['ra'] = 3.44616
prior['theta_jn'] = 2.5028
prior['psi'] = 0
prior['luminosity_distance'] = 40
```

When I run the analysis, I used the same sampler described in the following:

```
result_short = bilby.run_sampler(
likelihood, prior, sampler='dynesty', outdir='short', label="GW170817",
conversion_function=bilby.gw.conversion.generate_all_bns_parameters,
sample="unif", nlive=500, dlogz=3 # <- Arguments are used to make things fast - not recommended for general use
)
```

Unfortunately, I obtained the error message.

```
....
1695it [08:13, 1.95s/it, bound:0 nc: 39 ncall:1.7e+04 eff:10.2% logz-ratio=1150.87+/-0.17 dlogz:373.146>3]
1696it [08:13, 1.45s/it, bound:0 nc: 3 ncall:1.7e+04 eff:10.2% logz-ratio=1151.17+/-0.17 dlogz:372.889>3]
1697it [08:19, 2.67s/it, bound:0 nc: 81 ncall:1.7e+04 eff:10.1% logz-ratio=1151.46+/-0.17 dlogz:372.589>3]
1698it [08:22, 2.78s/it, bound:0 nc: 44 ncall:1.7e+04 eff:10.1% logz-ratio=1151.72+/-0.17 dlogz:372.290>3]
1699it [08:39, 7.13s/it, bound:0 nc:256 ncall:1.7e+04 eff:10.0% logz-ratio=1151.95+/-0.17 dlogz:372.029>3]
Exception while calling prior_transform function:
params: [0.17066496 0.86254559 1.14297206]
args: []
kwargs: {}
exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/dynesty/dynesty.py", line 860, in __call__
return self.func(x, *self.args, **self.kwargs)
File "/usr/local/lib/python3.7/dist-packages/bilby/core/sampler/dynesty.py", line 53, in _prior_transform_wrapper
return _priors.rescale(_search_parameter_keys, theta)
File "/usr/local/lib/python3.7/dist-packages/bilby/core/prior/dict.py", line 487, in rescale
return list(flatten([self[key].rescale(sample) for key, sample in zip(keys, theta)]))
File "/usr/local/lib/python3.7/dist-packages/bilby/core/prior/dict.py", line 487, in <listcomp>
return list(flatten([self[key].rescale(sample) for key, sample in zip(keys, theta)]))
File "/usr/local/lib/python3.7/dist-packages/bilby/core/prior/analytical.py", line 206, in rescale
self.test_valid_for_rescaling(val)
File "/usr/local/lib/python3.7/dist-packages/bilby/core/prior/base.py", line 188, in test_valid_for_rescaling
raise ValueError("Number to be rescaled should be in [0, 1]")
ValueError: Number to be rescaled should be in [0, 1]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-24-0a93c6f15491> in <module>
8 likelihood, prior, sampler='dynesty', outdir='short', label="GW170817",
9 conversion_function=bilby.gw.conversion.generate_all_bns_parameters,
---> 10 sample="unif", nlive=500, dlogz=3 # <- Arguments are used to make things fast - not recommended for general use
11 )
15 frames
/usr/local/lib/python3.7/dist-packages/bilby/core/prior/base.py in test_valid_for_rescaling(val)
186 tests = (valarray < 0) + (valarray > 1)
187 if np.any(tests):
--> 188 raise ValueError("Number to be rescaled should be in [0, 1]")
189
190 def __repr__(self):
ValueError: Number to be rescaled should be in [0, 1]
```

I just followed the tutorials provided by GWOSC to analyze the GW150914 (google colab), and changed the data from GW150914 to GW170817. In my previous post, I still use the waveform model ‘IMRPhenomPv2’, and it may not be proper to apply for the case of the neutron star merger. In the attached Jupyter notebook (Tuto_3_2_Parameter_estimation_for_compact_object_mergers.ipynb), now I consider the waveform model ‘IMRPhenomPv2_NRTidal’, but I still obtain the similar error message. By the way, these are the recommended versions of the packages installed in my tests: lalsuite==6.82 bilby==1.1.2 gwpy==2.0.2 matplotlib==3.2.2 dynesty==1.0.0.

**Can anyone have any comment to deal with my problem?**