Commit 30fd2378 authored by Gregory Ashton's avatar Gregory Ashton
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Minor improvemen to paper: abstract and figure labels

parent d09c9d1e
......@@ -65,15 +65,19 @@
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

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