Commit 30fd2378 authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Minor improvemen to paper: abstract and figure labels

parent d09c9d1e
...@@ -65,15 +65,19 @@ ...@@ -65,15 +65,19 @@
\date{\today} \date{\today}
\begin{abstract} \begin{abstract}
We detail methods to follow-up potential CW signals (as identified by We detail methods to follow-up potential CW signals (as identified by
wide-parameter space semi-coherent searches) leveraging MCMC optimisation of the wide-parameter space semi-coherent searches) leveraging MCMC optimisation of
$\mathcal{F}$-statistic. First, we demonstrate the advantages of such an the $\mathcal{F}$-statistic. Such a framework provides a unique advantage when
optimisation whilst increasing the coherence time, namely the ability to used during the `zoom' (in which the coherence time is increased aiming to
efficiently sample an evolving distribution and consider multiple modes. localise the fully-coherent candidate) in that several candidates can be
Subsequently, we illustrate estimation of parameters and the Bayes factor which effeciently followed up simultaneously. We describe an automated method to
can be used to understand the significance of the candidate. Finally, we explain define the number of zoom stages and verify such methods work in a Monte-Carlo
how the methods can be simply generalised to allow the waveform model to be study. More, MCMC optimisation naturally produces parameter estimation for the
transient or undergo glitches. 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} \end{abstract}
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Paper/single_glitch_F0F1_grid_2D.png

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Paper/single_glitch_F0F1_grid_2D.png

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Paper/single_glitch_F0F1_grid_2D.png
Paper/single_glitch_F0F1_grid_2D.png
Paper/single_glitch_F0F1_grid_2D.png
Paper/single_glitch_F0F1_grid_2D.png
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...@@ -2728,8 +2728,9 @@ class GridSearch(BaseSearchClass): ...@@ -2728,8 +2728,9 @@ class GridSearch(BaseSearchClass):
ax.set_xlim(x[0], x[-1]) ax.set_xlim(x[0], x[-1])
ax.set_ylim(y[0], y[-1]) ax.set_ylim(y[0], y[-1])
ax.set_xlabel(xkey) labels = {'F0': '$f$', 'F1': '$\dot{f}$'}
ax.set_ylabel(ykey) ax.set_xlabel(labels[xkey])
ax.set_ylabel(labels[ykey])
if xN: if xN:
ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(xN)) ax.xaxis.set_major_locator(matplotlib.ticker.MaxNLocator(xN))
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