revised README

README.md

@@ -1,21 +1,32 @@
 # Constraining weakly interacting massive particles with primordial black holes
 
+Code authors: Adam Coogan, Daniele Gaggero and Bradley Kavanagh.
+
 ## Reproducing results
 
 The figures can be remade with the following commands:
+
 * Figure 1:
+
     `python plot_pbh_fraction.py`
+
 * Figure 2:
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     `python plot_frequentist_limits.py -plot_limits`
+
 * Figure 3:
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     `python plot_frequentist_limits.py -plot_ps_diff`
 
-Computing the point-source limits requires making tables containing `p_gamma`, the probability that an individual PBH is detectable by ermi-LAT. The tables used for the analysis in the paper are contained in the directory `data/p_gammas/`. These may take a few hours to recompute. They can be regenerated with
+Computing the point-source limits requires making tables containing `p_gamma`, the probability that an individual PBH is detectable by Fermi-LAT. The tables used for the analysis in the paper are contained in the directory `data/p_gammas/`. These may take a few hours to recompute. They can be regenerated with
+
     `python generate_p_gamma_tables.py -test`
+
 With the `-test` flag, the `p_gamma` tables will be written to `data/p_gammas/test/` instead of overwriting the precomputed tables. The script can also be used to generate `p_gamma` tables over different `(m_dm, sv)` grids. 
 
 ## `bayesian` branch
-The Bayesian branch contains code for performing a Bayesian analysis of the cross section limits.
+
+This branch contains code for performing a Bayesian analysis of the cross section limits.
 * The notebook `plot_bayesian_limits.ipynb` generates Bayesian versions of figures 2 and 3 assuming a uniform prior on the cross section.
 * `submit_posterior_calcs_diff.py` generates tables of the posterior over a grid of `(m_dm, sv)` points for the diffuse analysis. The posterior is very simple: we marginalize over `f`, assume the background model for the EGB perfectly matches observations, and simply penalize the extragalactic flux from PBHs for exceeding zero using a Gaussian likelihood for each bin.
 * The analysis point-source analysis assumes that astrophysical sources can appear in the unassociated point source catalogue with an unknown rate, which we marginalize over assuming a Jeffreys prior. The script `submit_posterior_calcs_ps.py` generates tables of `p_gamma` and the posterior over an `(m_dm, sv)` grid.