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Gregory Ashton
PyFstat
Commits
30fd2378
Commit
30fd2378
authored
Dec 08, 2016
by
Gregory Ashton
Browse files
Minor improvemen to paper: abstract and figure labels
parent
d09c9d1e
Changes
3
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Paper/paper_cw_mcmc.tex
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30fd2378
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@@ 65,15 +65,19 @@
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@@ 65,15 +65,19 @@
\date
{
\today
}
\date
{
\today
}
\begin{abstract}
\begin{abstract}
We detail methods to followup potential CW signals (as identified by
We detail methods to followup potential CW signals (as identified by
wideparameter space semicoherent searches) leveraging MCMC optimisation of the
wideparameter space semicoherent 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 fullycoherent 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 MonteCarlo
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 fullycoherent candidate. Finally, we show that with minor modifications
the followup 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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pyfstat.py
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@@ 2728,8 +2728,9 @@ class GridSearch(BaseSearchClass):
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@@ 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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