Additional-Review-(marginalization-over-joint-population+EoS-uncertainty).md

@@ -30,7 +30,7 @@ p(m_\mathrm{src}) = p(m_\mathrm{det}) \left| \frac{dm_\mathrm{det}}{dm_\mathrm{s
 
 for each component mass, meaning that the total contribution to the weights should have been `(1+z)**+2` instead of `(1+z)**-2` as was actually implemented. However, as the redshift was small for the NSBH sources considered, this factor does not significantly affect our conclusions in any way.
 
-The new version of the code relies on the user to specify the draw probability, so this is rendered moot. Within the tests described below, this was implemented correctly within [this script](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/gw-distributions/test/o3-nsbh/src/convert-event-csv#L48). **Update link once merge request is accepted**
+The new version of the code relies on the user to specify the draw probability, so this is rendered moot. Within the tests described below, this was implemented correctly within [this script](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/master/test/o3-nsbh/src/convert-event-csv#L48).
 
 - [X] Reviewer signoff on implementation in example (AMF 28 August 2023)
 - [X] Reviewer signoff that in new version of the code, implementation of Jacobians should be done through inputs, not on this code base. (AMF 28 August 2023)
@@ -72,7 +72,7 @@ S = \int d\Lambda d\theta p(\theta, \Lambda|\mathrm{data}) \Theta(\theta) = \int
 ```
 
 and to do so we must normalize the sum over `\theta_i` to remove the factor of `p(data|Lambda)`.
-The correct sum is implemented within the updated code [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/gw-distributions/mmms/engine.py#L54) **Update link once merge request has been accepted**.
+The correct sum is implemented within the updated code [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/master/mmms/engine.py#L54).
 
 This difference can cause discrepencies between the old and the new code when there is a nontrivial marginalization over the population (i.e., when there is more than a single population sample `\Lambda_p`).
 These can be much larger than the statistical uncertainty from the finite number of Monte Carlo samples (see [below](#comparison-to-previous-results)), but have never been large enough to change the scientific conclusions drawn. Furthermore, [ApJ 915 L5 (2021)](https://iopscience.iop.org/article/10.3847/2041-8213/ac082e) only reported results assuming a fixed population (flat in source-frame component masses) and therefore this issue did not affect the published results.
@@ -94,7 +94,7 @@ A technical note deriving the Monte Carlo sums used to estimate the various prob
 
 Below, we compare results obtained for previous (published) LVK analyses with the output from the new version of the code. We focus on results published as part of the "exceptional event paper" describing the first detections of NSBH systems [[ApJ 915 L5 (2021)](https://iopscience.iop.org/article/10.3847/2041-8213/ac082e)].
 
-Previous results are stored [here](https://git.ligo.org/reed.essick/o3-nsbh-mmax-model-selection/-/tree/master/data) and results obtained with the updated code are stored [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/tree/gw-distributions/test/o3-nsbh/data). **Update link once merge request has been accepted**
+Previous results are stored [here](https://git.ligo.org/reed.essick/o3-nsbh-mmax-model-selection/-/tree/master/data) and results obtained with the updated code are stored [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/tree/master/test/o3-nsbh/data).
 The workflow used to generate the new results is also stored [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/tree/gw-distributions/test/o3-nsbh/src).
 
 Below, I compare the main output of the calculation: the probability that an object is consistent with a NS. Links point to where the (old/new) results are stored. I provide a comparison to all previous results computed for both GW200105 and GW200115.