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Paper/Examples/grided_frequency_search.py
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......@@ -28,10 +28,10 @@ data = pyfstat.Writer(
Delta=Delta, h0=h0, sqrtSX=sqrtSX)
data.make_data()
m = 0.001
m = 1
dF0 = np.sqrt(12*m)/(np.pi*duration)
DeltaF0 = 800*dF0
F0s = [F0-DeltaF0/2., F0+DeltaF0/2., dF0]
DeltaF0 = 30*dF0
F0s = [F0-DeltaF0/2., F0+DeltaF0/2., 1e-2*dF0]
F1s = [F1]
F2s = [F2]
Alphas = [Alpha]
......@@ -47,18 +47,19 @@ frequencies = np.unique(search.data[:, xidx])
twoF = search.data[:, -1]
#mismatch = np.sign(x-F0)*(duration * np.pi * (x - F0))**2 / 12.0
ax.plot(frequencies, twoF, 'k', lw=1)
ax.plot(frequencies, twoF, 'k', lw=0.8)
DeltaF = frequencies - F0
sinc = np.sin(np.pi*DeltaF*duration)/(np.pi*DeltaF*duration)
A = np.abs((np.max(twoF)-4)*sinc**2 + 4)
ax.plot(frequencies, A, '-r', lw=1)
ax.plot(frequencies, A, 'r', lw=1.2, dashes=(0.2, 0.2))
ax.set_ylabel('$\widetilde{2\mathcal{F}}$')
ax.set_xlabel('Frequency')
ax.set_xlim(F0s[0], F0s[1])
dF0 = np.sqrt(12*1)/(np.pi*duration)
xticks = [F0-10*dF0, F0, F0+10*dF0]
ax.set_xticks(xticks)
xticklabels = ['$f_0 {-} 10\Delta f$', '$f_0$', '$f_0 {+} 10\Delta f$']
xticklabels = [r'$f_0 {-} 10\Delta f(\tilde{\mu}=1)$', '$f_0$',
r'$f_0 {+} 10\Delta f(\tilde{\mu}=1)$']
ax.set_xticklabels(xticklabels)
plt.tight_layout()
fig.savefig('{}/{}_1D.png'.format(search.outdir, search.label), dpi=300)
Paper/Examples/transient_search_using_MCMC.py
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......@@ -16,7 +16,7 @@ data_tstart = tstart - duration
data_tend = data_tstart + 3*duration
tref = .5*(data_tstart+data_tend)
h0 = 5e-24
h0 = 3e-24
sqrtSX = 1e-22
transient = pyfstat.Writer(
......
Paper/bibliography.bib
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− 0
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......@@ -494,3 +494,62 @@ year = {2015}
year={1998},
publisher={Taylor \& Francis}
}
@Article{ hobbs2010,
author = {Hobbs, G. and Lyne, A.~G. and Kramer, M.},
doi = {10.1111/j.1365-2966.2009.15938.x},
issn = {00358711},
journal = {Monthly Notices of the Royal Astronomical Society},
keywords = {1 i n t,6000 years of pulsar,archive of pulsar
observations,contains over,general,pulsars,ro d u
c,rotational history,t i o n,the jodrell bank data,the
pulsar timing method},
month = feb,
number = {2},
pages = {1027-1048},
title = {{An analysis of the timing irregularities for 366
pulsars}},
url = {http://doi.wiley.com/10.1111/j.1365-2966.2009.15938.x},
volume = {402},
year = {2010}
}
@article{ashton2017,
title = {{Statistical characterization of pulsar glitches and their
potential impact on searches for continuous gravitational
waves}},
author = {Ashton, G. and Prix, R. and Jones, D. I.},
note = {In prep},
year = {2017}
}
@article{Papa2016,
title = {{Hierarchical follow-up of subthreshold candidates of an all-sky Einstein@Home search for continuous gravitational waves on LIGO sixth science run data}},
author = {{Papa}, M.~A. and {Eggenstein}, H-B. and {Walsh}, S. and {Di Palma}, I. and {Allen}, B. and {Astone}, P. and {Bock}, O. and {Creighton}, T.~D. and {Keitel}, D. and {Machenschalk}, B. and {Prix}, R. and {Siemens}, X. and {Singh}, A. and {Zhu}, S.~J. and {Schutz}, B.~F.},
journal = {Phys. Rev. D},
volume = {94},
issue = {12},
pages = {122006},
numpages = {14},
year = {2016},
month = {Dec},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.94.122006},
url = {http://link.aps.org/doi/10.1103/PhysRevD.94.122006}
}
@article{dupuis2005,
title = {Bayesian estimation of pulsar parameters from gravitational wave data},
author = {Dupuis, R\'ejean J. and Woan, Graham},
journal = {Phys. Rev. D},
volume = {72},
issue = {10},
pages = {102002},
numpages = {9},
year = {2005},
month = {Nov},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.72.102002},
url = {http://link.aps.org/doi/10.1103/PhysRevD.72.102002}
}
Paper/definitions.tex
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......@@ -9,7 +9,7 @@
\newcommand{\tstart}{t_{\rm start}}
\newcommand{\tend}{t_{\rm end}}
\newcommand{\Nglitches}{N_{\rm g}}
\newcommand{\Tspan}{T_{\rm span}}
\newcommand{\Tspan}{T}
\newcommand{\Tcoh}{T_{\rm coh}}
\newcommand{\tref}{t_{\rm ref}}
\newcommand{\Nseg}{N_{\rm seg}}
......@@ -23,7 +23,7 @@
\newcommand{\ho}{h_0}
\newcommand{\homax}{\ho^{\rm max}}
\newcommand{\Bsn}{B_{\rm S/N}}
\newcommand{\Pic}{\Pi_{\rm c}}
\newcommand{\AmplitudePrior}{\Pi_\mathcal{A}}
\newcommand{\mutilde}{\tilde{\mu}}
\newcommand{\Sn}{S_{\rm n}}
\newcommand{\V}{\mathcal{V}}
......
Paper/grided_frequency_search_1D.png
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  • 2-up
  • Swipe
  • Onion skin
Paper/macros.tex
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......@@ -12,7 +12,6 @@
\def\BasicExamplehzero{1.0{\times}10^{-24}}
\def\BasicExamplenburn{50.0}
\def\BasicExamplenprod{50.0}
\def\DirectedMC{}GlitchNoiseN{970.0}
\def\DirectedMCNoiseN{1.0{\times}10^{4}}
\def\DirectedMCNoiseOnlyMaximum{52.4}
\def\DirectedMConeGlitchNoiseN{10000}
......
Paper/paper_cw_mcmc.tex
+ 259
− 265
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......@@ -51,7 +51,7 @@
\begin{document}
\title{MCMC follow-up methods for continuous gravitational wave candidates}
\title{MCMC follow-up of continuous gravitational wave candidates}
\author{G. Ashton}
\email[E-mail: ]{gregory.ashton@ligo.org}
......@@ -67,18 +67,15 @@
\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. 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.
We describe methods to follow-up potential continuous gravitational wave
candidates (as identified by wide-parameter space semi-coherent searches)
leveraging MCMC optimisation of the $\mathcal{F}$-statistic. Such a framework
provides a unique advantage in both following-up standard signals (i.e. during
the `zoom-stage' in which the coherence time is increased aiming to localise
the fully-coherent candidate and in parameter estimation) as well as follow-up
of non-standard waveforms. In particular, we demonstrate provide a
glitch-robust statistic for follow-up of glitching continuous gravitational
wave signals.
\end{abstract}
......@@ -91,8 +88,8 @@ underdetected.
\section{Introduction}
A possible target for the advanced gravitational wave detector network of LIGO
and Virgo are long-lived periodic sources called continuous waves (CWs).
A target for the advanced gravitational wave detector network of LIGO
and Virgo are long-lived periodic sources called continuous gravitational waves (CWs).
Rapidly rotating nonaxisymmetric neutron stars are potentially capable of
producing detectable CWs which last much longer than typical observation spans.
There exists three well known sources of the nonaxisymmetry: `mountains',
......@@ -107,49 +104,51 @@ nonaxisymmetric source produces a strain in the detector $h(t, \A, \blambda)$;
where $\A{=}\{h_0, \cos\iota, \psi, \phi_0\}$ is a vector of the four
\emph{amplitude-parameters} (expressed in `physical coordinates') and
$\blambda$ is a vector of the \emph{Doppler-parameters} consisting of the
sky-location, frequency $f$, and any spindown terms required by the search.
sky-location, frequency $f$, and any spindown terms $\{\dot{f}, \ddot{f},
\ldots\}$.
CW searches typically use a fully-coherent matched-filtering methods whereby a
template (the signal model at some specific set of parameters) is convolved
against the data resulting in a detection statistic. Since the signal
CW searches often use a fully-coherent matched-filtering methods whereby a
\emph{template} (the signal model at some specific set of parameters) is
convolved against the data resulting in a detection statistic (in this work, we
will consider the $\F$-statistic, defined in \citet{jks1998}). Since the signal
parameters are unknown, it is usual to perform this matched filtering over a
grid of points. Three search categories can be identified: \emph{targeted}
searches for a signal from a known electromagnetic pulsar where the Doppler
parameters are considered `known'; \emph{directed} searches in which the
location is known, but not the frequency and spin-down (i.e. searching for the
neutron star in a supernova remnant which does not have a known pulsar); and
\emph{all-sky} searches where none of the parameters are considered known.
Searching over more parameters amounts to an increase in the dimension of the
search space. Since the density of grid points required to resolve a signal
scales inversely with total coherence time $\Tcoh$ (the span of data used in
a fully-coherent matched filter), wide-parameter searches (such as the all-sky)
with many search dimensions over long durations of data are computationally
demanding.
grid of points to maximise the detection statistic. Three search categories
can be identified: \emph{targeted} searches for a signal from a known
electromagnetic pulsar where the Doppler parameters are considered `known';
\emph{directed} searches in which the location is known, but not the frequency
and spin-down (i.e. searching for the neutron star in a supernova remnant
which does not have a known pulsar); and \emph{all-sky} searches where none of
the parameters are considered known. Searching over more parameters amounts to
an increase in the dimension of the search space. Since the density of grid
points required to resolve a signal scales inversely with total coherence time
$\Tcoh$ (the span of data used in a fully-coherent matched filter),
wide-parameter searches (such as the all-sky) with many search dimensions over
long durations of data are computationally infeasible.
At a fixed computing cost, it has been shown (see for example \citep{brady1998,
prix2012}) that a semi-coherent search is more sensitive for unknown signals
than a fully-coherent search. While the exact details of how the search works
depends on the implementation, semi-coherent search work by splitting the total
observation span $\Tspan$ into $\Nseg$ segments (each lasting for $\Tcoh$) and
in each segment computes the fully-coherent detection statistic; the
semi-coherent detection statistic is then computed by some combination of all
segments summed at the same point in parameter space. Fundamentally, this gain
in sensitivity is because the width of a peak in the detection statistic due to
a signal is inversely proportional to the coherence time: shorter coherence times
make the peak wider and hence the a lower density of templates. This idea was
first proposed by \citet{brady2000} along with the first implementation, the
`Stack-slide' search. Since then, several modifications such as the
`Hough-transform' \citep{krishnan2004, astone2014}, and the `Powerflux' method
(first described in \citet{allyskyS42008}) have been proposed, implemented and
applied to gravitational wave searches.
depends on the implementation, a semi-coherent search involves by dividing the
total observation span $\Tspan$ into $\Nseg$ segments (each lasting for
$\Tcoh$) and in each segment computes the fully-coherent detection statistic;
the semi-coherent detection statistic is then computed by some combination of
all segments summed at the same point in parameter space. Fundamentally, this
gain in sensitivity is because the width of a peak in the detection statistic
due to a signal is inversely proportional to the coherence time: shorter
coherence times make the peak wider and hence a lower density of templates is
required. This idea was first proposed by \citet{brady2000} along with the
first implementation, the `Stack-slide' search. Since then, several
modifications such as the `Hough-transform' \citep{krishnan2004, astone2014},
and the `Powerflux' method (first described in \citet{allyskyS42008}) have been
proposed, implemented and applied to gravitational wave searches.
Wide parameter space searches produce a list of candidates with an associated
detection statistic which passes some threshold. In order to verify these
candidates, they are subjected to a \emph{followed-up}: a process of increasing
candidates, they are subjected to a \emph{follow-up}: a process of increasing
the coherence time, eventually aiming to calculate a fully-coherent detection
statistic over the maximal span of data. In essence, the semi-coherent search
is powerful as it spreads the significance of a candidate over a wider area of
parameter space, so a follow-up attempts to reverse this process and recover
parameter space, the follow-up attempts to reverse this process and recover
the maximum significance and tightly constrain the candidate parameters. The
original hierarchical follow-up of \citet{brady2000} proposed a two-stage method
(an initial semi-coherent stage followed directly by a fully-coherent search.
......@@ -161,35 +160,48 @@ computational cost.
The first implementation of a two-stage follow-up was given by
\citet{shaltev2013} and used the Mesh Adaptive Direct Search algorithm for
optimisation. At the time of publication, this method was limited to two stages
and could not handle binary parameters \comment{(I think these limitations have
now been removed, but I can't find a publication)}, however these are practical
limitations which can \comment{(have?)} be overcome.
\comment{Add something on multiple modes?}
optimisation. A multi-stage gridded approach was presented in \citet{Papa2016}.
In this paper, we propose an alternative hierarchical follow-up procedure using
Markov-Chain Monte-Carlo (MCMC) as the optimisation tool. In terms of the
semi-coherent to follow-up procedure, an MCMC tool is advantages due to it's
ability to trace the evolution multiple modes simultaneously through the
follow-up procedure and allow the optimisation to decide between them without
arbitrary cuts. In addition, MCMC methods also provide two further
advantages: they can calculate directly calculate Bayes factors, the significance
of a candidate and because they are `grid-less' one can arbitrarily vary the
waveform model without requiring an understanding of the underlying topology.
We will exploit this latter property to propose an additional step in the
follow-up procedure which allows for the CW signal to be either a transient-CW
(a periodic signal lasting $\mathcal{O}(\textrm{hours-weeks})$) or to undergo
glitches (as seen in pulsars).
Markov-Chain Monte-Carlo (MCMC) as the optimisation tool. An MCMC tool is
advantages due to its ability to trace the evolution of multiple modes
simultaneously through the follow-up procedure and allow the optimisation to
decide between them without arbitrary cuts.
The follow-up of canonical CW signals is a well studied problem and, provided
they behave as expected, the maximum loss of detection statistic (i.e. the
template bank mismatch) can be calculated. However, nature does not always
behave as expected and it is plausible that the CW signals in the data may
differ from the templates used in wide-parameter space searches. There are of
course an unlimited number of ways this may manifest given our ignorance of
neutron stars, but from the study of pulsars three obvious mechanisms present
themselves: \emph{transient-}, \emph{glitching-}, and \emph{noisy-} signals.
A search method for long-duration transient signals (i.e. signals which
last from hours to weeks) was first presented by
\citet{prix2011}, in Sec.~\ref{sec_transients} we will describe how MCMC
optimisation can be used to perform parameter estimation using this method.
In \citet{ashton2017}, we noted the need for a glitch-robust follow-up method
(i.e. a search capable of finding signals which undergo sudden changed in the
rotation frequency as seen in pulsars); to respond to this in Sec.~\ref{sec_glitches}
we provide such a statistic, demonstrate its use on simulated glitching
signals, and discuss significance estimation. For noisy-signals, specifically
motivated by the observation of so-called timing noise in radio pulsars (see
\citet{hobbs2010} for a review), in \citet{ashton2015} we determined that these
where unlikely to be of immediate concern.
The methods introduced in this paper have been implemented in the
\texttt{python} package \texttt{pyfstat}. All examples in this work, along with
tutorials can be found at
\url{https://gitlab.aei.uni-hannover.de/GregAshton/PyFstat}.
We begin in Section~\ref{sec_hypothesis_testing} with a review of search
methods from a Bayesian perspective. Then in
Section~\ref{sec_MCMC_and_the_F_statistic} we introduce the MCMC optimisation
procedure and give details of our particular implementation. In
Section~\ref{sec_follow_up} we will illustrate applications of the method and
provide a prescription for choosing the setup. In Sections~\ref{sec_transients}
and \ref{sec_glitches} we demonstrate how searches can be performed for either
transient or glitches CWs before we finally conclude in
Section~\ref{sec_conclusion}.
Section~\ref{sec_follow_up} we describe the zoom follow-up procedure and test
its efficient. In Sections~\ref{sec_transients} and \ref{sec_glitches} we
demonstrate how searches can be performed for either transient or glitches CWs
before we finally conclude in Section~\ref{sec_conclusion}.
\section{Hypothesis testing}
\label{sec_hypothesis_testing}
......@@ -204,13 +216,13 @@ way to write the odds as
O_{\rm S/N} \equiv \frac{P(\Hs| x, I)}{P(\Hn| x, I)} =
\Bsn(x| I) \frac{P(\Hs| I)}{P(\Hn | I)},
\end{equation}
where the second factor is the prior odds while the first factor is the
where the second factor is the \emph{prior odds} while the first is the
\emph{Bayes factor}:
\begin{equation}
\Bsn(x| I) = \frac{P(x| \Hs, I)}{P(x| \Hn, I)}.
\end{equation}
Typically, we set the prior odds to unity such that it is the Bayes factor
which determines our confidence in the signal hypothesis. In this work we will
which determines our confidence in the signal hypothesis. In this work, we will
therefore discuss the Bayes factor with the implied assumption this is
equivalent to the odds, unless we have a good reason to change the prior odds.
......@@ -236,17 +248,18 @@ where
is the \emph{likelihood-ratio}.
At this point, we can appreciate the problems of searching for unknown signals:
one has four amplitude parameters and several Doppler parameters (three plus
the number of spin-down and binary parameters) over which this integral must be
performed. If a single signal exists in the data, this corresponds to a single
peak in the likelihood-ratio, but at an unknown location. Therefore, one must
must first search for peaks (candidates), and then subsequently analyse their
significance. If one has prior knowledge this can be used; for example in
targeted searches for gravitational waves from known pulsars emitting CWs from
a mountain the Doppler parameters are considered known collapsing their
integral to a single evaluation.
\subsection{The $\F$-statistic}
one has four amplitude parameters and several Doppler parameters over which
this integral must be performed. If a single signal exists in the data, this
corresponds to a single peak in the likelihood-ratio, but at an unknown
location. Therefore, one must must first search for peaks (candidates), and
then subsequently analyse their significance. If one has prior knowledge this
can be used; for example in targeted searches for gravitational waves from
known pulsars emitting CWs from a mountain the Doppler parameters are
considered known collapsing their integral to a single evaluation (see for
example the method proposed by \citet{dupuis2005}).
\comment{Should we cite other known-pulsar methodologies?}
\subsection{The $\F$-statistic: a frequentist perspective}
For directed and all-sky searches, a common method introduced by
\citet{jks1998} to reduce the parameter space is the maximum-likelihood
......@@ -257,9 +270,11 @@ this likelihood-ratio with respect to the four amplitude parameters results
(c.f.~\citet{prix2009}) in a maximised log-likelihood given by $\F(x|
\blambda)$: the so-called $\F$-statistic. Picking a particular set of Doppler
parameters $\blambda$ (the template) one can then compute a detection statistic
(typically $2\F$ is used) which can be used to quantify the significance of the
template. Usually this is done by calculating a corresponding false alarm rate,
the probability of seeing such a detection statistic in Gaussian noise.
(typically $\twoFtilde$ is used\footnote{Note that the `tilde' denote that it
is a fully-coherent quantity while we use a `hat' to denote a semi-coherent
quantity}) which can be used to quantify the significance of the template.
Usually this is done by calculating a corresponding false alarm rate, the
probability of seeing such a detection statistic in Gaussian noise.
Calculations of the significance are possible due to the properties of the
detection statistic: in Gaussian noise, it can be shown \citep{jks1998,
......@@ -281,60 +296,52 @@ where $(h|h)$ is the inner product of the signal with itself (see for example
the detector and $\mathcal{N}$ is the number of detectors.
\subsection{Using the $\F$-statistic to compute a Bayes factor} At first, it
\subsection{The $\F$-statistic: a Bayesian perspective}
At first, it
appears that the $\F$-statistic is independent of the Bayesian framework since
it was first derived directly from the likelihood. However, it was shown by
\citet{prix2009} that if we marginalise over the four amplitude parameters of
Equation~\eqref{eqn_full_bayes}, choosing a prior $\Pic$ such that
\begin{equation}
P(\A| \Hs, \Pic, I) \equiv \left\{
\begin{array}{ll}
C & \textrm{ for } \ho < \homax \\
0 & \textrm{ otherwise}
\end{array}
\right..
\end{equation}
it was first derived directly from the likelihood. However, it was shown in
\citet{prix2009, prix2011} that if we marginalise over the four amplitude parameters of
Equation~\eqref{eqn_full_bayes} (using the ``$\F$-statistic prior'' \citep{prix2011},
which we denote by $\AmplitudePrior$)
then the integral, when $\homax \gg 1$, is a Gaussian integral and can be
computed analytically as
\begin{align}
\Bsn(x| \Pic, \blambda, I) & \equiv
\begin{split}
\Bsn(x| \blambda, \AmplitudePrior, I) & \equiv
\int
\mathcal{L}(x ;\A, \blambda)
P(\A| \Hs, I) d\A
P(\A| \Hs, \AmplitudePrior, I) d\A
\label{eqn_B_S_N}
\\
& = \frac{C (2\pi)^{2} e^{\F(x| \blambda)}}
{\sqrt{\textrm{det} \mathcal{M}}},
& \propto e^{\tilde{\F}(x| \blambda)}
\end{split}
\end{align}
where $C$ is a normalisation constant, $\textrm{det}\mathcal{M}$ is an antenna
pattern factor dependent on the sky-position and observation period, and
$\mathcal{F}$ is the frequentist log-likelihood of \citet{jks1998}. This result
where $\mathcal{F}$ is exactly the $\F$-statistic, first derived
as a maximised log-likelihood \citep{jks1998}. This result
demonstrates that the $\mathcal{F}$-statistic is proportional to the log-Bayes
factors when calculated with a uniform prior on the amplitude parameters and
fixed Doppler parameters.
As such, we can define the Bayes-factor of Equation~\eqref{eqn_full_bayes} as
\begin{equation}
\Bsn(x| \Pic, I) = \int
\Bsn(x| \Pic, \blambda, I) P(\blambda| \Hs, I)
\Bsn(x| \AmplitudePrior, I) = \int
\Bsn(x| \AmplitudePrior, \blambda, I) P(\blambda| \Hs, I)
d\blambda,
\end{equation}
or neglecting the constants
\begin{equation}
\Bsn(x| \Pic, I) \propto \int
e^{\F(x| \blambda)} P(\blambda| \Hs, I)
\Bsn(x| \AmplitudePrior, I) \propto \int
e^{\tilde{\F}(x| \blambda)} P(\blambda| \Hs, I)
d\blambda.
\label{eqn_bayes_over_F}
\end{equation}
Formulating the significance of a CW candidate in this way is pragmatic in that
there exists a wealth of well-tested tools \citep{lalsuite} capable of
computing the $\mathcal{F}$-statistic for CW signals, transient-CWs, and CW
signals from binary systems; these can be leveraged to compute
Equation~\eqref{eqn_bayes_over_F}, or adding in the constant
$\Bsn(x| \Pic)$ itself. The disadvantage to this method is that
we are forced to use the prior $\Pic$, which was shown by \citet{prix2009} to
be unphysical.
there exists a wealth of well-tested tools (i.e. \texttt{LALSuite}
\citep{lalsuite}) capable of computing the $\mathcal{F}$-statistic for CW
signals, transient-CWs, and CW signals from binary systems; these can be
leveraged to compute Equation~\eqref{eqn_bayes_over_F}.
\section{MCMC and the $\mathcal{F}$-statistic}
\label{sec_MCMC_and_the_F_statistic}
......@@ -346,23 +353,22 @@ given given some data $x$ for some hypothesis $\H$. Namely, Bayes rule
P(\theta| x, \H, I) \propto P(x| \theta, \H, I)P(\theta| \H, I),
\label{eqn_bayes_for_theta}
\end{equation}
is used to evaluate proposed jumps from one point in parameter to other points;
jumps which increase this probably are accepted with some probability. The
algorithm, proceeding in this way, is highly efficient at resolving peaks in
high-dimension parameter spaces.
is used to evaluate proposed steps in parameter space. After some
initilisations, the samples (points visited during the stepping process) can be
used as samples of the posterior distribution. The algorithm is highly
efficient at resolving peaks in high-dimension parameter spaces.
At this point, we note the equivalence of Equation~\eqref{eqn_bayes_for_theta}
to the integrand of Equation~\eqref{eqn_bayes_over_F}:
\begin{equation}
P(\blambda | x, \Pic, \Hs, I)
%=\Bsn(x| \Pic, \blambda) P(\blambda| \Hs I).
P(\blambda | x, \AmplitudePrior, \Hs, I)
%=\Bsn(x| \AmplitudePrior, \blambda) P(\blambda| \Hs I).
\propto e^{\F(x| \blambda)} P(\blambda| \Hs I),
\label{eqn_lambda_posterior}
\end{equation}
where $e^{\F}$ is the likelihood.
In this work, we will focus on using MCMC methods to sample this, the
posterior distribution of the Doppler parameters and moreover compute the
final Bayes factor.
posterior distribution of the Doppler parameters.
\subsection{The \texttt{emcee} sampler}
......@@ -390,11 +396,11 @@ modification, the parallel-tempered ensemble sampler. A parallel
tempered MCMC simulation, first proposed by \citet{swendsen1986}, runs
$\Ntemps$ simulations in parallel with the likelihood in the $i^{\rm th}$
parallel simulation is raised to a power of $1/T_{i}$ where $T_i$ is referred
to as the temperature. As such, Equation~\eqref{eqn_lambda_posterior} is
to as the \emph{temperature}. As such, Equation~\eqref{eqn_lambda_posterior} is
written as
\begin{equation}
P(\blambda | T_i, x, \Pic, \Hs, I)
%=\Bsn(x| \Pic, \blambda)^{T_i} P(\blambda| \Hs I).
P(\blambda | T_i, x, \AmplitudePrior, \Hs, I)
%=\Bsn(x| \AmplitudePrior, \blambda)^{1/T_i} P(\blambda| \Hs I).
\propto (e^{\F(x| \blambda)})^{T_i} P(\blambda| \Hs I).
\end{equation}
Setting $T_0=1$ with $T_i > T_0 \; \forall \; i > 1$, such that the lowest
......@@ -403,25 +409,25 @@ temperatures the likelihood is broadened (for a Gaussian likelihood, the
standard deviation is larger by a factor of $\sqrt{T_i}$). Periodically, the
algorithm swaps the position of the walkers between the different
temperatures. This allows the $T_0$ chain (from which we draw samples of the
posterior) to efficiently sample from multi-modal posteriors. This introduces
posterior) to efficiently sample from multi-modal posteriors. This method introduces
two additional tuning parameters, the number and range of the set of
temperatures $\{T_i\}$, we will discuss their significance when relevant.
\subsection{Parallel tempering: estimating the Bayes factor}
\comment{Greg: I don't think we actually need this since we aren't using the Bayes factor}
In addition, parallel-tempering also offers a robust method to estimate the
Bayes factor of Equation~\eqref{eqn_bayes_over_F}. If we define
$\beta\equiv1/T$, the inverse temperature and $Z(\beta)\equiv \Bsn(x| \Pi_{\rm
c}, I)$, then as noted by \citet{goggans2004} for the general case, we may
$\beta\equiv1/T$, the inverse temperature and $Z(\beta)\equiv \Bsn(x| \AmplitudePrior, I)$, then as noted by \citet{goggans2004} for the general case, we may
write
\begin{align}
\frac{1}{Z} \frac{\partial Z}{\partial \beta}=
\frac{
\int \Bsn(x| \Pic, \blambda)^{\beta}
\log(\Bsn(x| \Pic, \blambda))P(\blambda| I)
\int \Bsn(x| \AmplitudePrior, \blambda)^{\beta}
\log(\Bsn(x| \AmplitudePrior, \blambda))P(\blambda| I)
d\blambda
}
{
\int \Bsn(x| \Pic, \blambda)^{\beta})P(\blambda| I)
\int \Bsn(x| \AmplitudePrior, \blambda)^{\beta})P(\blambda| I)
d\blambda
}
\end{align}
......@@ -429,20 +435,20 @@ The right-hand-side expresses the average of the log-likelihood at $\beta$. As
such, we have
\begin{align}
\frac{\partial \log Z}{\partial \beta} =
\langle \log(\Bsn(x| \Pic, \blambda) \rangle_{\beta}
\langle \log(\Bsn(x| \AmplitudePrior, \blambda) \rangle_{\beta}
\end{align}
The log-likelihood are a calculated during the MCMC sampling. As such, one
The log-likelihood values are calculated during the MCMC sampling. As such, one
can numerically integrate to get the Bayes factor, i.e.
\begin{align}
\log \Bsn(x| \Pic, I) = \log Z = \int_{0}^{1}
\langle \log(\Bsn(x| \Pic, \blambda) \rangle_{\beta} d\beta.
\log \Bsn(x| \AmplitudePrior, I) = \log Z = \int_{0}^{1}
\langle \log(\Bsn(x| \AmplitudePrior, \blambda) \rangle_{\beta} d\beta.
\end{align}
In practice, we use a simple numerical quadrature over a finite ladder of
$\beta_i$ with the smallest chosen such that choosing a smaller value does not
change the result beyond other numerical uncertainties. Typically, getting
accurate results for the Bayes factor requires a substantially larger number of
temperatures than are required for efficiently sampling multi-modal
distributions. Therefore, it is recommended that one uses a small number of
distributions. Therefore, it is recommended that one uses a few
temperatures during the search stage, and subsequently a larger number of
temperatures (suitably initialised close to the target peak) when estimating
the Bayes factor.
......@@ -455,9 +461,9 @@ poorly if the posterior has multiple small maxima with only a small number of
large maxima which occupy a small fraction of the prior volume. Later on, in
Section~\ref{sec_topology} we will show that for a signal in Gaussian noise our
log-likelihood $\F$ will have a single peak due to the signal which is
surrounded by other peaks particular to given noise realisation. The relative
size of the these peaks to the signal peak depends on the SNR. Howeever,
regardless of the strengt of the signal peak, if the width of the signal peak
surrounded by other smaller peaks particular to given noise realisation. The relative
size of the these peaks to the signal peak depends on the SNR. However,
regardless of the strength of the signal peak, if the width of the signal peak
is small compared to the prior volume, the sampler will get `stuck' on the
local maxima and be inefficient at finding the global maxima. This problem is
exacerbated in higher-dimensional search spaces where the volume fraction of
......@@ -467,14 +473,14 @@ the signal scales with the exponent of the number of dimensions.
In a traditional CW search which uses a grid of templates (also known as a
template bank), the spacings of the grid are chosen such that the loss of
signal to noise ratio (SNR) is bounded to less than $\mu$, the
SNR is bounded to less than $\mu$, the
\emph{template-bank mismatch}. The optimal choice of grid then consists of
minimising the computing cost while respecting this minimum template-bank
mismatch or vice-verse (for examples see \citet{pletsch2010, prix2012,
wette2013, wette2015}). We will now discuss how the work on setting up these
grids can be applied to the problem of determining whether the setup is
appropriate for an MCMC method: i.e. given the prior volume do we expect a
signal to occupy a non-negligible volume?
signal to occupy a non-negligible fraction of the volume.
For a fully-coherent $\F$-statistic search on data containing Gaussian noise
and a signal with Doppler parameters $\blambdaSignal$, the template-bank
......@@ -489,23 +495,23 @@ parameter (c.f. Equation~\ref{eqn_twoF_expectation}) at $\blambda_l$, given
that the signal is at $\blambdaSignal$; as such $\tilde{\rho}(\blambdaSignal;
\blambdaSignal)$ is the perfectly-matched non-centrality parameter. For a
fully-coherent search, this non-centrality parameter is equivalent to
fully-coherent matched-filter signal to noise ratio (SNR). However, as noted by
fully-coherent matched-filter SNR. However, as noted by
\citet{leaci2015}, this is true for the fully-coherent case only. Therefore,
we choose to use the non-centrality parameter which easily generalises to the
semi-coherent case.
To make analytic calculations of the mismatch possible, as first shown by
To make analytic calculations of the template-bank mismatch possible, as first shown by
\citet{brady1998}, the mismatch can be approximated by
\begin{equation}
\mutilde(\blambda, \Delta\blambda) \approx
\tilde{g}_{ij}\Delta\lambda^{i}\Delta\lambda^{j}
\tilde{g}_{ij}^\phi \Delta\lambda^{i}\Delta\lambda^{j}
+ \mathcal{O}\left(\Delta\blambda^{3}\right)
\label{eqn_mutilde_expansion}
\end{equation}
where we use index notation with an implicit summation over repeated indices.
Here, $\tilde{g}_{ij}^{\phi}$ is the \emph{phase-metric} given by
\begin{align}
\tilde{g}_{ij}(\blambda) =
\tilde{g}_{ij}^\phi (\blambda) =
\langle
\partial_{\Delta\lambda^{i}}\phi
\partial_{\Delta\lambda^{j}}\phi
......@@ -555,7 +561,7 @@ with $\smax{=}1$ (i.e. the frequency and spin-down components) are given by
\end{array}
\right]
\textrm{ for } i, j \in [f, \dot{f}].
\end{equation}.
\end{equation}
Accurately approximating the sky components of the metric is non-trivial, but
was accomplished by \citet{wette2013} for the fully-coherent case. In
......@@ -587,7 +593,7 @@ of the directed search, and $\Delta f^{(s)}$ is the extent of the frequency and
spin-down range(s) searched.
The metric volume $\V$ is the approximate number of templates required to cover
the the given Doppler parameter volume at a fixed mismatch of $\approx 1$. As
given Doppler parameter volume at a fixed mismatch of $\approx 1$. As
such, its inverse gives the approximate (order of magnitude) volume fraction of
the search volume which would be occupied by a signal. This can therefore be
used as a proxy for determining if an MCMC search will operate in a regime where
......@@ -595,7 +601,7 @@ it is efficient (i.e. where the a signal occupies a reasonable fraction of the
search volume).
The volume $\V$ combines the search volume from all search dimensions. However,
let us know discuss how we can delve deeper to understand how each dimension
let us now discuss how we can delve deeper to understand how each dimension
contributes to the total product. This is done by noticing that the metric has
a particular block form:
\begin{align}
......@@ -636,7 +642,6 @@ $\widetilde{2\F}$ is 4 in Gaussian noise alone, but proportional to the square
of the non-centrality parameter (or SNR) in the presence of a signal. To
illustrate this, let us consider $\widetilde{2\F}$ as a function of $f$ (the
template frequency) if there exists a signal in the data with frequency $f_0$.
We will fix all the other Doppler parameters so that they are perfectly
matched. Such an example can be calculated analytically, taking the
matched-filtering amplitude (Equation~(11) of \citep{prix2005}) with
......@@ -653,10 +658,10 @@ a perfectly matched signal (when $f=f_0$).
In Figure~\ref{fig_grid_frequency} we compare the analytic prediction of
Equation~\eqref{eqn_grid_prediction} with the value computed numerically from
simulating a signal in Gaussian noise. This is done over a frequency range of
$\sim20\Delta f$ where $\Delta f$ is the frequency difference between a signal
and the template with $\tilde{\mu}=1$ as calculated from
$\sim20\Delta f$ where $\Delta f(\mutilde=1)$ is the frequency difference between a signal
and the template with the metric-mismatch $\tilde{\mu}=1$ as calculated from
Equation~\eqref{eqn_mutilde_expansion}. As expected, close to the signal
frequency $f_0$ the detection statistic peaks with a a few local secondary
frequency $f_0$ the detection statistic peaks with a few local secondary
maxima. Away from this frequency, in Gaussian noise, we see many local maxima
centered around the expected value of 4.
\begin{figure}[htb]
......@@ -664,27 +669,28 @@ centered around the expected value of 4.
\caption{Comparison of the analytic prediction of
Equation~\eqref{eqn_grid_prediction} (in red) with the value computed
numerically from simulating a signal with frequency $f_0$ in Gaussian noise (in
black). $\Delta f$ is the frequency difference corresponding to a
black). $\Delta f(\mutilde=1)$ is the frequency difference corresponding to a
metric-mismatch of unity.}
\label{fig_grid_frequency}
\end{figure}
\subsection{Example: signal in noise}
\subsection{Example: MCMC optimisation for a signal in noise}
In order to familiarise the reader with the features of an MCMC search, we will
now describe a simple directed search (over $f$ and $\dot{f}$) for a simulated
signal in Gaussian noise. The signal will have a frequency of
$\BasicExampleFzero$~Hz and a spin-down of $\BasicExampleFone$~Hz/s, all other
Doppler parameters are known and so are irrelevant. Moreover, the signal has an
amplitude $\BasicExamplehzero$~Hz$^{-1/2}$ while the Gaussian noise has
$\sqrt{\Sn}=\BasicExampleSqrtSn$~Hz$^{-1/2}$ such that the signal has a depth
of $\BasicExampleDepth$.
Doppler parameters are considered known. Moreover, the signal has an amplitude
$\BasicExamplehzero$~Hz$^{-1/2}$ while the Gaussian noise has
$\sqrt{\Sn}=\BasicExampleSqrtSn$~Hz$^{-1/2}$ such that the signal has a
\emph{depth} of $\sqrt{\Sn}/h_0 = \BasicExampleDepth$.
First, we must define a prior for each search parameter Typically, we recommend
either a uniform prior bounding the area of interest, or a normal distribution
centered on the target and with some well defined width. However, to ensure
that the MCMC simulation has a reasonable chance at finding a peak, one should
consider the corresponding metric-volume given in
centered on the target and with some well defined width. In defining the prior
distributions, one must ensure
that the MCMC simulation has a reasonable chance at finding a peak. To do so,
we can use the corresponding metric-volume given in
Equation~\eqref{eqn_metric_volume}. For this example, we will use a uniform
prior with a frequency range of $\Delta f=\BasicExampleDeltaFzero$~Hz and a
spin-down range of $\Delta \fdot=\BasicExampleDeltaFone$~Hz/s both centered on
......@@ -702,21 +708,21 @@ and
such that $\V\approx\BasicExampleV$ (note that $\Vsky$ does not contribute
since we do not search over the sky parameters). This metric volume indicates
that the signal will occupy about 1\% of the prior volume, therefore the MCMC
is expected to work. Alternative priors will need careful thought about how to
is expected to work efficiently. Alternative priors will need careful thought about how to
translate them into a metric volume: for example using a Gaussian one could use
the standard deviation as a proxy for the allowed search region.
In addition to defining the prior, one must also consider how to
\emph{initialise} the walkers. If the prior genuinely represents the stated
prior knowledge, the usual solution is to initialise the walkers from the
prior: that is the starting position is drawn from the prior. However,
instances do occur when one would like to initialise the walkers from a
different distribution. For example, if one only needs to estimate the evidence
(given a particular prior), but is aware from previous searches that the only
significant peak lies in a small area of parameter space, one could initialise
the walkers in a small cluster close to that area. In this example, we
initialise the walkers from the prior such that they have the chance to explore
the entire prior volume.
\emph{initialise} the walkers of the ensemble sampler. If the prior genuinely
represents the stated prior knowledge, the usual solution is to initialise the
walkers from the prior: that is the starting position is drawn from the prior.
However, instances do occur when one would like to initialise the walkers from
a different distribution. For example, if one only needs to estimate the
evidence (given a particular prior), but is aware from previous searches that
the only significant peak lies in a small area of parameter space, one could
initialise the walkers in a small cluster close to that area. In this example,
we initialise the walkers from the prior such that they have the chance to
explore the entire prior volume.
Having defined the prior, the final setup step is to define the number of
\emph{burn-in} and \emph{production} steps the sampler should take and the
......@@ -735,7 +741,7 @@ of the total number of steps. The red portion of steps are burn-in and hence
discarded, from this plot we see why: the walkers are initialised from the
uniform prior and initially spend some time exploring the whole parameter space
before converging. The fact that they converge to a single unique point is due
to the strength of the signal (substantially elevating the likelihood about
to the strength of the signal (substantially elevating the likelihood above
that of Gaussian fluctuations) and the tight prior which was quantified through the
metric volume $\V$. The production samples, colored black, are only taken once
the sampler has converged - these can be used to generate posterior plots.
......@@ -747,12 +753,11 @@ noise, searching over the frequency and spin-down. The upper two panels show
the position of all walkers as a function of the number of steps for the
frequency and spin-down; when they are colored red the samples are discarded as
burn-in (the first 100 steps), while when they are colored black they are used
as production samples. The bottom panel shows the distribution of
$\widetilde{2\F}$ taken from the production samples.}
as production samples.}
\label{fig_MCMC_simple_example}
\end{figure}
\section{Testing convergence}
\subsection{Testing convergence}
In Figure~\ref{fig_MCMC_simple_example}, it is easy to see by eye that that the
MCMC simulation has converged during the burn-in. However, choosing the number
......@@ -763,11 +768,11 @@ methods (diagnostics) also exist to test convergence and can therefore provide
the utility to prematurely stop the burn-in period. As such one can then simply
choose a large number of steps (as compared to a typical burn-in for the given
problem) and expect that in most cases these will not all be used. Moreover, if
such a large number of searches are to be run such that inspection of each MCMC
a large number of searches are to be run such that inspection of each MCMC
simulation is impossible, the convergence tests can easily be checked
numerically.
\subsection{The Gelman \& Rubin statistic}
\subsubsection{The Gelman \& Rubin statistic}
We will use the ``Gelman \& Rubin statistic" \citep{gelman1992} to assess
convergence. This method, developed in the context of single-walker samplers
(i.e. not ensemble samplers) requires multiple independent simulations to be
......@@ -775,7 +780,7 @@ run, each initialised from an overdispersed estimate of the target
distribution. The statistic, the \emph{potential scale reduction factor}
(PSRF), is calculated for each scalar quantity of interest and estimates the
amount by which the scale factor of the target distribition (approximated by a
Student t distribution) might shrink if the simulation where run indefinitely.
Student-t distribution) might shrink if the simulation where run indefinitely.
This method makes a normality assumption on the posterior distribution, which
in testing appears to be reasonble for all parameters except the transient
start time and durations (discussed later in Sec.~\ref{sec_transients}).
......@@ -785,7 +790,7 @@ summarized by the mean and variance (or standard deviation), even if the
posterior distributions are not themselves believed to be normally distributed.
\comment{Note I have not yet fully implemented the method, for simplicity I'm
assuming df -> infty}
assuming df $\rightarrow$ infty}
In this setting, we depart from the original description by using the walkers
of the ensemble sampler in place of independent MCMC simulations. Moreover,
......@@ -804,7 +809,7 @@ determination convergence, but we echo the conclusions of \citet{cowles1996marko
that no convergence diagnostic can say with certainty that the posterior
samples are truly representative of the underlying stationary distribution.
\subsection{Example}
\subsubsection{Example}
In Figure~\ref{fig_MCMC_convergence_example}, we illustrate our implementation
of the Rubin-Gelman statistic for a fully-coherent search of data including
......@@ -846,13 +851,13 @@ fully-coherently. We begin by rewriting Equation~\eqref{eqn_lambda_posterior},
the posterior distribution of the Doppler parameters, with the explicit
dependence on the coherence time $\Tcoh$:
\begin{equation}
P(\blambda | \Tcoh, x, \Pic, \Hs, I)
%=\Bsn(x| \Tcoh, \Pic, \blambda) P(\blambda| \Hs I).
P(\blambda | \Tcoh, x, \AmplitudePrior, \Hs, I)
%=\Bsn(x| \Tcoh, \AmplitudePrior, \blambda) P(\blambda| \Hs I).
\propto e^{\hat{\F}(x| \Tcoh, \blambda)} P(\blambda| \Hs I).
\end{equation}
Introducing the coherent time $\Tcoh$ as a variable provides a free parameter
which adjusts width of signal peaks in the likelihood. Therefore, a natural way
Introducing the coherence time $\Tcoh$ as a variable provides a free parameter
which adjusts the width of signal peaks in the likelihood. Therefore, a natural way
to perform a follow-up is to start the MCMC simulations with a short coherence
time (such that the signal peak occupies a substantial fraction of the prior
volume) and then subsequently incrementally increasing this coherence time in a
......@@ -865,15 +870,16 @@ parameters, namely the ladder of coherence times $\Tcoh^{i}$, where $i \in [0,
In some ways, this bears a resemblance to `simulated annealing', a method in
which the likelihood is raised to a power (the inverse temperature) and
subsequently `cooled'. The important difference being that the semi-coherent
likelihood is wider at short coherence times, rather than flatter as in the
case of high-temperature simulated annealing stages. For a discussion and
examples of using simulated annealing in the context of CW searches see
\citet{veitch2007}.
subsequently `cooled' \comment{Add citation?}. The important difference being
that the semi-coherent likelihood is wider at short coherence times, rather
than flatter as in the case of high-temperature simulated annealing stages. For
a discussion and examples of using simulated annealing in the context of CW
searches see \citet{veitch2007}.
Of course in practice, we can't arbitrarily vary $\Tcoh^i$, but rather the
In practice, we can't arbitrarily vary $\Tcoh^i$, but rather the
number of segments at each stage $\Nseg^{i}\equiv \Tspan/\Tcoh^{i} \in
\mathbb{N}$. Ideally, the ladder of segment should be chosen to ensure that
\mathbb{N}$. For an efficient zoom follow-up, we want to choose the ladder of
coherence times to ensure that
the metric volume at the $i^{th}$ stage $\V_i \equiv \V(\Nseg^i)$ is a constant
fraction of the volume at adjacent stages. That is we define
\begin{equation}
......@@ -881,17 +887,11 @@ fraction of the volume at adjacent stages. That is we define
\end{equation}
where $\mathcal{R} \ge 1$ as $\Tcoh^{i+1} > \Tcoh^{i}$.
For the MCMC simulations to be succesful, this initial bounding
box, given the segment setup which produced the candidate, must be small
compared to the width of the signal (at that segment setup). If we start out
follow-up with the same search setup used in the source search, i.e. we set
$\Nseg^0$ equal to the number of segments used in the input search, then for
the MCMC simulation to work, we require that $\V(\Nseg^{0}) \sim \mathcal{O}(100)$.
Given a fixed prior on the Doppler parameters and a fixed span of data, the
metric volume $\V_{\Nstages}$ for $\Tcoh^{\Nstages} = \Tspan$ is fixed, or in
terms of the number of segments, $\Nseg^{\Nstages} = 1$.
We can therefore define a minimisation problem:
Assuming then that the candidate comes with a suitably small prior bounding box,
such that $\V(\Nseg^{0}) \sim \mathcal{O}(100)$, we define $\Nseg^0$ as the
number of segments used in the searc which found the candidate.
We can therefore define a minimisation problem for $\Nseg^{i+1}$ given $\Nseg^{i}$:
\begin{align}
\min_{\Nseg^{i+1} \in \mathbb{N}}
\left| \log\mathcal{R} + \log\V(\Nseg^{i+1}) - \log\V(\Nseg^i)\right|
......@@ -900,16 +900,13 @@ which can be solved numerically to a specified tolerance. Due to the difficulty
in solving such a minimisation with the integer constrain on $\Nseg^{i+1}$ we
simply solve it as a real scalar, and then round to the nearest integer. We now
have a method to generate a ladder of $\Nseg^{i}$ which keep the ratio of
volume fractions fixed. Starting with $\Nseg^{\Nstages}$ = 1, we generate
$\Nseg^{\Nstages-1}$ such that $\V^{\Nstages-1} < \V^{\Nstages}$ and
subsequently iterate. Finally we must define $\V^{\rm min}$ as the stopping
criterion: a metric volume such that the initial stage will find a signal. This
stopping criterion itself will set $\Nstages$; alternatively one could set
$\Nstages$.
volume fractions fixed.
We have now defined a method to automate the task of choosing the ladder of
coherence times. This requires only two tuning parameters, the ratio between
stages $\mathcal{R}$ and the minimum metric volume $\V^{\min}$.
We run the minimisation problem, given a value of $\mathcal{R}$, iterating
until $\Nseg^{\Nstages} = 1$ at which point we stop: we have generated the
ladder of $\Nseg^i$ given a single tuning parameter, $\mathcal{R}$. Note that
$\Nstages$ is not known prior to runing the iterative minimization probelm, and
will in general depend on both the initial search prior bounding box and setup.
\subsection{Example}
......@@ -923,10 +920,10 @@ in the noise.
First, we must define the setup for the run. Using $\mathcal{R}=10$ and
$\V^{\rm min}=100$ our optimisation procedure is run and proposes the setup
layed out in Table~\ref{tab_follow_up_run_setup}. In addition, we show the
number of steps taken at each stage.
number of MCMC steps taken at each stage.
\begin{table}[htb]
\caption{The search setup used in Figure~\ref{fig_follow_up}, generated with
$\mathcal{R}=10$ and $\V^{\rm min}=100$.}
$\mathcal{R}=10$ and $\Nseg^0=100$.}
\label{tab_follow_up_run_setup}
\input{follow_up_run_setup}
\end{table}
......@@ -946,7 +943,7 @@ prior volume, therefore the MCMC simulation quickly converges to it. Subsequentl
each time the number of segments is reduced, the peak narrows and the samplers
similarly converge to this new solution. At times it can appear to be inconsistent,
however this is due to the changing way that the Gaussian noise adds to the signal.
Eventually, the walkers all converge to the true signal.
Eventually, the walkers all converge to the simulated signal.
\begin{figure}[htb]
\centering
\includegraphics[width=0.5\textwidth]{follow_up_walkers}
......@@ -1000,7 +997,7 @@ they are not sufficiently strong to rise above the noise.
\subsubsection{Follow-up candidates from a directed search}
\label{sec_directed_follow_up}
In a directed search, the sky location parameters $\alpha$ and $\delta$ are
In a directed search, the sky-location parameters $\alpha$ and $\delta$ are
fixed - in our study we fix them on the location of the Crab pulsar, but this
choice is arbitrary and holds no particular significance. The ouput of a
semi-coherent gridded directed search would provide the grid-point with the
......@@ -1033,7 +1030,7 @@ realisations.}
The behaviour of the follow-up is independent of the exact frequency and
spin-down values used (this is not true for an all-sky follow-up as discussed
in Section~\ref{sec_all_sky_follow_up}). As such, we can, without loss of
generality define our Monte-Carlo follow-up in the following way. First, we
generality, define our Monte-Carlo follow-up in the following way. First, we
select an arbitrary frequency and spindown of $f_0=30$~Hz and
$\dot{f}_0=10^{-10}$Hz/s and a surrounding uncertainty box $\Delta f$ and
$\Delta \dot{f}$ chosen such that the uncertainty in frequency and spindown are
......@@ -1046,8 +1043,7 @@ Having generated the data given the prescription above, we proceed to perform a
hierarchical MCMC follow-up. Given the data span and initial bounding box, we
compute the optimal heirarchical setup, the details of which are given in
Table~\ref{tab_directed_MC_follow_up}, this setup was used for all MC
simulations. This table also lists the number of steps, which in this case we
chose to be 10 - quite a small number of steps for a typical MCMC simulation.
simulations.
\begin{table}[htb]
\caption{Run-setup for the directed follow-up Monte-Carlo study, generated with
......@@ -1062,9 +1058,9 @@ The signal is considered `detected' if $\widetilde{2\F}^{\rm max} >
Figure~\ref{fig_directed_MC_follow_up} we plot the the fraction of the MC
simulations which where recovered and compare this against the theoretical
maximum, given the threshold. The figure demonstrates that the recovery power
of the MCMC follow-up shows only a small margin of loss compared to the
theoretical maximum. \comment{Probably best to run with more steps and see if
this doesn't remove the loss}
of the MCMC follow-up shows negligible differences compared to the
theoretical maximum.
\begin{figure}[htb]
\centering
\includegraphics[width=0.45\textwidth]{directed_recovery}
......@@ -1074,6 +1070,7 @@ Table~\ref{tab_directed_MC_follow_up}.}
\label{fig_directed_MC_follow_up}
\end{figure}
\comment{Need to add discussion on the pmax 'analytic fit'}
\subsubsection{Follow-up candidates from an all-sky search}
\label{sec_all_sky_follow_up}
......@@ -1113,10 +1110,10 @@ choise, but is consisent with the values chosen in \citet{shaltev2013}.
\caption{Histogram of the recovered $\widetilde{2\F}$ values applying the
all-sky follow-up routine to $\AllSkyMCNoiseN$ simulated Gaussian noise
realisations.}
\label{fig:}
\label{fig_hist_AllSkyMCNoiseOnly}
\end{figure}
Producing \CHECK{1000} indepedant MC simulations we the perform a follow-up on
Producing \CHECK{1000} indepedant MC simulations we then perform a follow-up on
each using the setup given in Table~\ref{tab_allsky_MC_follow_up}. The
resulting recovery fraction as a function of the injected signal depth is given
in Figure~\ref{fig_allsky_MC_follow_up}.
......@@ -1140,23 +1137,11 @@ Table~\ref{tab_directed_MC_follow_up}.}
\label{fig_allsky_MC_follow_up}
\end{figure}
\comment{Need to add a conclusion}
\section{Alternative waveform models: transients}
\label{sec_transients}
In a grided search, the template bank is constructed to ensure that a canonical
CW signal (i.e. when it lasts much longer than the observation span and has a
phase evolution well-described by a Equation~\eqref{eqn_phi}) will be recovered
with a fixed maximum loss of detection statistic; this loss can be described as
the `template-bank mismatch'. In addition to this mismatch, CW searches may
experience a mismatch if the waveform model differs from the matched-filter
template. There are of course an unlimited number of ways this may manifest
given our ignorance of neutron stars, but from studying pulsars three obvious
mechanisms present themselves: transient, glitching, and noisy waveforms. In
the this section we will discuss the first of these, then in
Section~\ref{sec_glitches} the second, we discussed the effect of random
jitters in the phase evolution (noisy waveforms) in \citet{ashton2015} and
concluded it was unlikely to be of immediate concern.
The term \emph{transient-CWs} refers to periodic gravitational wave signals
with a duration $\mathcal{O}(\textrm{hours-weeks})$ which have a
phase-evolution described by Equation~\eqref{eqn_phi}. \citet{prix2011} coined
......@@ -1171,7 +1156,7 @@ rectangular window or an exponential decay). These methods are implemented in
the code-base used by our sampler to compute the likelihood and therefore we
can expose these search parameters to our MCMC optimisation. In the following
we will detail a simple example showing when it may be appropriate to search for
a transient signal and how it is handles by the MCMC sampler.
a transient signal and how it is handled by the MCMC sampler.
We simulate a signal in Gaussian noise at a depth of 10. If the signal where to
be continuous (i.e. last for the entire duration of the data span), it should
......@@ -1185,7 +1170,7 @@ the portion of data for which it is `on' is $5162/3\approx1720$.
Running a fully-coherent MCMC search over the whole data span, we find a peak
in the likelihood, but with a detection statistic of $\widetilde{2\F}=596$;
this equates to a mismatch of $\approx0.9$: we have lost more significance due
this equates to a mismatch of $\approx0.9$: we have lost significance due
to the inclusion of noise-only data into the matched filter.
In a real search, we cannot know beforehand what the $h_0$ of the signal will
......@@ -1206,7 +1191,7 @@ time.}
\label{fig_transient_cumulative_twoF}
\end{figure}
Having identified that the putative signal may fact be transient, an extension
Having identified that the putative signal may in fact be transient, an extension
of the follow-up procedure is to search for these transient parameters. In our
MCMC method, these require a prior. For the window-function, one must define it
either to be rectangular or exponential: one could run both and then use the
......@@ -1218,7 +1203,10 @@ normal distribution placing greater weight on shorter transients. The choice of
prior can allow the transient signal to overlap with epochs outside of the data
span, in such instances if the likelihood can be computed they are allowed, but
if the likelihood fails (for example if there is no data) the likelihood is
returns as $-\infty$. Putting all this together, we run the sampler on the
returns as $-\infty$.
\comment{Probably this is a bit too much detail}
Putting all this together, we run the sampler on the
simulated transient signal and obtain the posterior estimates given in
Figure~\ref{fig_transient_posterior}. The resulting best-fit has a
$\widetilde{2\F}\approx 1670$, in line with the expected value. Comparing the
......@@ -1261,7 +1249,7 @@ provided the number of glitches in a signal during a search is less than say
3, the initial search should still find the signal. However, that signal may
look like a transient signal, or be `lost' during the follow up process.
This work motivates the existence of a \emph{glitch-robust} follow-up search
\citet{ashton2017} motivates the existence of a \emph{glitch-robust} follow-up search
which is capable of fitting for the glitch parameters. As such, we will now
discuss how the $\F$-statistic optimisation routine discussed in this work can
be used to search for glitching signals.
......@@ -1283,7 +1271,7 @@ An ideal glitch search would include these new parameters into the phase model
itself and compute the $\F$-statistic fully-coherently i.e. rewriting
Equation~\eqref{eqn_B_S_N} as
\begin{multline}
\Bsn(x| \{\tglitch^\ell\}, \{\delta\blambda^\ell\}, \Pic, \blambda, I) \equiv \\
\Bsn(x| \{\tglitch^\ell\}, \{\delta\blambda^\ell\}, \AmplitudePrior, \blambda, I) \equiv \\
\int
\mathcal{L}(x ;\A, \blambda, \{\tglitch^\ell\}, \{\delta\blambda^\ell\})
P(\A| \Hs, I) d\A
......@@ -1291,15 +1279,8 @@ P(\A| \Hs, I) d\A
where the glitch parameters subsequently modify the phase model. However, we
propose instead the following pragmatic approach: let us instead use a
semi-coherent detection statistic with the epoch between \emph{segments} as
$\tglitch^\ell$, i.e.
\begin{multline}
\Bsn(x| \tglitch, \delta\blambda, \Pic, \blambda, I) \equiv \\
\sum_{\ell=0}^{\Nglitches}
\int_{\tglitch^\ell}^{\tglitch^{\ell+1}}
\mathcal{L}(x ;\A, \blambda+\delta\blambda^\ell)
P(\A| \Hs, I) d\A
\end{multline}
or, written in terms of the $\mathcal{F}$-statistic,
$\tglitch^\ell$, That is, we define a glitch-robust semi-coherent
$\mathcal{F}$-statistic:
\begin{multline}
\twoFhat(N_{\rm glitch}, \lambda, \{\tglitch^\ell\}, \{\delta\blambda^{\ell}\})= \\
\sum_{\ell=0}^{N_{\rm glitch}}
......@@ -1307,8 +1288,7 @@ or, written in terms of the $\mathcal{F}$-statistic,
t_{\rm glitch}^\ell, t_{\rm glitch}^{\ell+1})
\label{eqn_glitch_likelihood}
\end{multline}
\comment{Need to think about how to connect these two statements and if the
first one is required}
\comment{Need to think about how to present this better}
This simplistic method leverages readily available and tested code with a loss
of sensitivity over the fully-coherent search by a factor $\sqrt{\Nglitches+1}$
......@@ -1318,7 +1298,7 @@ search.
The general methodology for a glitch-robust statistic (i.e.
Eq.\eqref{eqn_glitch_likelihood}) could be implemented in any number of ways,
the most straightforward of course beging a gridded search over the glitch
the most straightforward of course being a gridded search over the glitch
parameters. However, for each glitch this introduces at least two new
parameters to search over. For reasonable priors on the unknown glitch
parameters, the likelihood peak occupies a small fraction of the total search
......@@ -1329,7 +1309,7 @@ search space occupied should not be so small as to make application of the MCMC
procedures described in this work inefficient. On the contrary, we find in
practise that the MCMC parameter estimation to be highly efficient.
\subsection{Case study: directed glitching signals}
\subsection{Case study: directed search for a glitching signal}
As an illustration of the use of this method, we simulate a signal in Gaussian
noise which undergoes a single glitch; the properties of this signal and the
......@@ -1397,19 +1377,33 @@ to the prior range.}
The glitch-robust statistic is similar to a semi-coherent
$\mathcal{F}$-statistic, but with additional degrees of freedom due to the
glitch parameters. As such, to compute a $p$-value (or false alarm rate) for a
given $\twoFhat$ we must understand the distribution of $\twoFhat(\Nglitches)$ in
noise. We follow the same prescription laid out in Sec.~\ref{sec_MCS_zoom},
namely we repeatedly run a single glitch search on independent 100~day data
span with Gaussian noise. Taking the maximum $\twoFhat$ from each search, we
plot the resulting histogram in Figure~\ref{fig_single_glitch_twoFhat_histgram}.
given $\twoFhat$ we must understand the distribution of $\twoFhat(\Nglitches)$
in noise. In general this will depend on the choice of prior and number of
glitches; we will now demonstrate the general characteristics for a 100~day
duration of data searching for a signal with 1 and 2 glitches.
We follow a similar prescription to that laid out in Sec.~\ref{sec_MCS_zoom},
namely we repeatedly run the $\Nglitches$-glitch search on independent 100~day
data span with Gaussian noise. Taking the maximum $\twoFhat$ from each search,
we plot the resulting histogram in
Figure~\ref{fig_single_glitch_twoFhat_histgram}.
\begin{figure}[htb]
\centering
\includegraphics[width=0.5\textwidth]{single_glitch_noise_twoF_histogram}
\includegraphics[width=0.5\textwidth]{glitch_noise_twoF_histogram}
\caption{The distribution of $\twoFhat$ }
\label{fig_single_glitch_twoFhat_histgram}
\end{figure}
\comment{Need to attempt to understand this behaviour analytically}
%This demonstrates that as the number of glitches is increased, the distribution
%in noise is shifted to large $\twoFhat$ value. This is expected since for
%
%Since the $\twoFhat$ for a
%perfectly matched signal is constant regardless of the number of segments (or
%glitches), this is precisely why semi-coherent searches are less sensitive for
%larger numbers of segments.
\section{Conclusion}
\label{sec_conclusion}
......
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