Minor improvemen to paper: abstract and figure labels

Paper/paper_cw_mcmc.tex

@@ -65,15 +65,19 @@
 \date{\today}
 
 \begin{abstract}
+
 We detail methods to follow-up potential CW signals (as identified by
-wide-parameter space semi-coherent searches) leveraging MCMC optimisation of the
-$\mathcal{F}$-statistic. First, we demonstrate the advantages of such an
-optimisation whilst increasing the coherence time, namely the ability to
-efficiently sample an evolving distribution and consider multiple modes.
-Subsequently, we illustrate estimation of parameters and the Bayes factor which
-can be used to understand the significance of the candidate. Finally, we explain
-how the methods can be simply generalised to allow the waveform model to be
-transient or undergo glitches.
+wide-parameter space semi-coherent searches) leveraging MCMC optimisation of
+the $\mathcal{F}$-statistic. Such a framework provides a unique advantage when
+used during the `zoom' (in which the coherence time is increased aiming to
+localise the fully-coherent candidate) in that several candidates can be
+effeciently followed up simultaneously. We describe an automated method to
+define the number of zoom stages and verify such methods work in a Monte-Carlo
+study. More, MCMC optimisation naturally produces parameter estimation for the
+final fully-coherent candidate. Finally, we show that with minor modifications
+the follow-up may allow for the CW waveform to be transient or undergo
+glitches; this may allow the discovery of signals which would otherwise go
+underdetected.
 
 \end{abstract}
 

pyfstat.py

@@ -2728,8 +2728,9 @@ class GridSearch(BaseSearchClass):
 
         ax.set_xlim(x[0], x[-1])
         ax.set_ylim(y[0], y[-1])
-        ax.set_xlabel(xkey)
-        ax.set_ylabel(ykey)
+        labels = {'F0': '$f$', 'F1': '$\dot{f}$'}
+        ax.set_xlabel(labels[xkey])
+        ax.set_ylabel(labels[ykey])
 
         if xN:
             ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(xN))