\documentclass[aps, prd, twocolumn, superscriptaddress, floatfix, showpacs, nofootinbib, longbibliography]{revtex4-1} % Fixes Adbobe error (131) \pdfminorversion=4 % Ams math \usepackage{amsmath} \usepackage{amssymb} % Packages for setting figures \usepackage[]{graphicx} \usepackage{subfig} % Colors \usepackage{color} % Bibliography \usepackage{natbib} \usepackage{placeins} \usepackage{multirow} \usepackage{hhline} % get hyperlinks %\usepackage{hyperref} % Tables \newcommand{\specialcell}[2][c]{% \begin{tabular}[#1]{@{}c@{}}#2\end{tabular}} \input{definitions.tex} % For editing purposes: remove before submition \usepackage[normalem]{ulem} %% only added for 'strikeout' \sout \usepackage[usenames,dvipsnames]{xcolor} \newcommand{\dcc}{LIGO-{\color{red}}} %% ---------- editing/commenting macros: make sure all a cleared at the end! ---------- \newcommand{\mygreen}{\color{green!50!black}} \newcommand{\addtext}[1]{\textcolor{green!50!black}{#1}} \newcommand{\meta}[1]{\addtext{#1}} \newcommand{\CHECK}[1]{\textcolor{red}{#1}} \newcommand{\strike}[1]{\textcolor{red}{\sout{#1}}} \newcommand{\comment}[1]{\textcolor{red}{[#1]}} \newcommand{\replace}[2]{\strike{#1}\addtext{$\rightarrow$ #2}} %% ---------- end: editing/commenting macros ---------------------------------------- \begin{document} \title{MCMC follow-up methods for continuous gravitational wave candidates} \author{G. Ashton} \email[E-mail: ]{gregory.ashton@ligo.org} \affiliation{Max Planck Institut f{\"u}r Gravitationsphysik (Albert Einstein Institut) and Leibniz Universit\"at Hannover, 30161 Hannover, Germany} \author{R. Prix} \affiliation{Max Planck Institut f{\"u}r Gravitationsphysik (Albert Einstein Institut) and Leibniz Universit\"at Hannover, 30161 Hannover, Germany} \date{\today} \begin{abstract} We detail methods to follow-up potential CW signals (as identified by wide-parameter space semi-coherent searches) leverging 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 distrubution and consider multiple modes. Subsequently, we illustrate estimation of parameters and the Bayes factor which can be used to understand the signficance of the candidate. Finally, we explain how the methods can be simply generalised to allow the waveform model to be transient or undergo glitches. \end{abstract} \pacs{04.80.Nn, 97.60.Jd, 04.30.Db} \input{git_tag.tex} \date{\commitDATE; \commitIDshort-\commitSTATUS, \dcc} \maketitle \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). 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', precession, and r-mode oscillations; each of which make a prediction for the scaling between $\nu$, the NS spin frequency and $f$, the gravitational wave frequency. In any case, observing neutron stars through their gravitational wave emmission would provide a unique astrophysical insight and has hence motivated numerous searches. As shown by \citet{jks1998}, the gravitational wave signal from a 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. 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 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. 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 propotional to the cohrence 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. 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 the coherence time, eventually aiming to calculate a fully-coherent detection statistic over the maximal span of data. In essense, 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 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-soherent search. However, it was shown in a numerical study by \citet{cutler2005} that allowing an arbitrary number of semi-coherent stages before the final fully-coherent stage can significantly improve the efficiency: ultimately they concluded that three semi-coherent stages provide the best trade-off between sensitivity and 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?} In this paper, we propose an alternative hierarchical follow-up procudure using Markov-Chain Monte-Carlo (MCMCM) 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 simultaneuosly through the follow-up procudedure and allow the optimisation to decide between them without arbitrary cuts. In addition, MCMC methods also provide two further advatanges: they can calculate directly calculate Bayes factors, the significance of a candidate and because they are `gridless' one can arbitrarily vary the waveform model without requring an understanding of the underlying topology. We will exploit this latter property to propose an additional step in the follow-up procudure 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). 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 producedure 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{Hypothesis testing} \label{sec_hypothesis_testing} \subsection{Bayes factors} Given some data $x$ and a set of background assumptions $I$, we formulate two hypotheses: $\Hn$, the data contains solely Gaussian noise and $\Hs$, the data contains an additive mixture of noise and a signal $h(t; \A, \blambda)$. In order to make a quantitative comparison, we use Bayes theorum in the usual way to write the odds as \begin{equation} 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 \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 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. We can rewrite the Bayes factor in terms of the two sets of signal parameters as \begin{equation} \Bsn(x| I) = \frac{P(x, \A, \blambda|\Hs, I)} {P(\A| \Hs, I)P(\blambda| \Hs, I)P(x| \Hn, I)}. \end{equation} Marginalising over the two sets of parameters we find that \begin{equation} \Bsn(x| I)= \iint \mathcal{L}(x; \A, \blambda) P(\A| \Hs, I)P(\blambda| \Hs, I) d\blambda d\A \label{eqn_full_bayes} \end{equation} where \begin{equation} \mathcal{L}(x; \A, \blambda) \equiv \frac{P(x |\A, \blambda, \Hs, I)}{P(x| \Hn, I)}, \label{eqn_likelihood} \end{equation} 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} For directed and all-sky searches, a common method introduced by \citet{jks1998} to reduce the parameter space is the maximum-likelihood approach. In this approach (often referred to as `frequentist'), one starts by defining the likelihood-ratio, Equation~\eqref{eqn_likelihood}, which in this context is a \emph{matched-filtering} amplitude. Then, analytically maximising 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 (typicaly $2\F$ 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. Calculations of the significance are possible due to the properties of the detection statistic: in Gaussian noise, it can be shown \citep{jks1998, cutlershutz2005} that $\widetilde{2\F}$ follows a chi-squared distribution with 4 degrees of freedom. In the presence of a signal, $\widetilde{2\F}$ is still chi-squared with 4 degrees of freedom, but has a non-centrality parameter $\tilde{\rho}^{2}$ such that its expectation value is \begin{equation} \textrm{E}[\widetilde{2\F}(x; \blambda)] = 4 + \tilde{\rho}(x; \blambda)^2. \label{eqn_twoF_expectation} \end{equation} The non-centrality parameter in this context is the SNR of the matched-filter given by \begin{equation} \rho^{2} = (h | h) \propto \frac{h_0^2}{\Sn}\Tcoh \mathcal{N} \end{equation} where $(h|h)$ is the inner product of the signal with itself (see for example \citet{prix2009}), $\Sn$ is a (frequency-dependent) measure of the noise in the detector and $\mathcal{N}$ is the number of detectors. \subsection{Using the $\F$-statistic to compute a Bayes factor} 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} 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 \int \mathcal{L}(x ;\A, \blambda) P(\A| \Hs, I) d\A \\ & = \frac{C (2\pi)^{2} e^{\F(x| \blambda)}} {\sqrt{\textrm{det} \mathcal{M}}}, \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 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) d\blambda, \end{equation} or neglecting the constants \begin{equation} \Bsn(x| \Pic, I) \propto \int e^{\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 levereged 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. \section{MCMC and the $\mathcal{F}$-statistic} \label{sec_MCMC_and_the_F_statistic} The MCMC class of optimisation tools are formulated to solve the problem of infering the posterior distribution of some general model parameters $\theta$ given given some data $x$ for some hypothesis $\H$. Namely, Bayes rule \begin{equation} 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 probabily are accepted with some probability. The algorithm, proceeding in this way, 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). \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. \subsection{The \texttt{emcee} sampler} In this work we will use the \texttt{emcee} ensemble sampler \citep{foreman-mackay2013}, an implementation of the affine-invariant ensemble sampler proposed by \citet{goodman2010}. This choice addresses a key issue with the use of MCMC sampler, namely the choice of \emph{proposal distribution}. At each step of the MCMC algorithm, the sampler generates from some distribution (known as the proposal-distribution) a jump in parameter space. Usualy, this proposal distribution must be `tuned' so that the MCMC sampler effeciently walks the parameter space without either jumping too far off the peak, or taking such small steps that it takes a long period of time to traverse the peak. The \texttt{emcee} sampler addresses this by using an ensemble, a large number ${\sim}100$ parallel \emph{walkers}, in which the proposal for each walker is generated from the current distribution of the other walkers. Moreover, by applying an an affine transformation, the efficiency of the algorithm is not diminished when the parameter space is highly anisotropic. As such, this sampler requires little in the way of tuning: a single proposal scale and the number of steps to take. \subsection{Parallel tempering: sampling multimodal posteriors} Beyond the standard ensemble sampler, we will also use one further 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 written as \begin{equation} P(\blambda | T_i, x, \Pic, \Hs, I) %=\Bsn(x| \Pic, \blambda)^{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 temperature recovers Equation~\eqref{eqn_lambda_posterior} while for higher temperatures the likelihood is broadened (for a Gaussian likelihood, the standard devitation is larger by a factor of $\sqrt{T_i}$). Periodically, the algorithem 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 two additional tuning parameters, the number and range of the set of temperatures $\{T_i\}$, we will discuss their signficance when relevant. \subsection{Parallel tempering: estimating 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 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) d\blambda } { \int \Bsn(x| \Pic, \blambda)^{\beta})P(\blambda| I) d\blambda } \end{align} 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} \end{align} The log-likelihood are a 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. \end{align} In practise, 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 effeciently sampling multi-modal distributions. Therefore, it is recomended that one uses a small number of temperatures during the search stage, and subsequently a larger number of temperatures (suitably initialised close to the target peak) when estimating the Bayes factor. \subsection{The topology of the likelihood} We intend to use the $\F$-statistic as our log-likelihood in MCMC simulations, but before continuing, it is worthwhile to acquaint ourselves with the typical behaviour of the log-likelihood by considering a specific example. As shownn in Equation~\eqref{eqn_twoF_expectation}, the expectation of $\widetilde{2\F}$ is 4 in Gaussian noise alone, but proportional to the square of the SNR in the presense 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 assume that all other Doppler parameters are perfectly matched. Such an example can be calculated analytically, taking the matched-filtering amplitude (Equation~(11) of \citep{prix2005}) with $\Delta\Phi(t) = 2\pi(f - f_0) t$, the expectation of $\widetilde{2\F}$ as a function of the template frequency $f$ is given by \begin{equation} \textrm{E}[\widetilde{2\F}](f) = 4 + (\textrm{E}[\widetilde{2\F_0}] -4)\textrm{sinc}^{2}(\pi(f-f_0)\Tcoh)) \label{eqn_grid_prediction} \end{equation} where $\textrm{E}[\widetilde{2\F_0}]$ is the expected $\widetilde{2\F}$ for 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. As expected, close to the signal frequency $f_0$ the detection statistic peaks with a 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] \centering \includegraphics[width=0.45\textwidth]{grided_frequency_search_1D} \caption{Comparison of the analytic prediction of Equation~\eqref{eqn_grid_prediction} (in red) with the value computed numerically from simulating a signal in Gaussian noise (in black). \comment{Need to explain ticks}} \label{fig_grid_frequency} \end{figure} \subsection{Limitations of use} In general, MCMC samplers are highly effective in generating samples of the posterior in multi-dimensional parameter spaces. However, they will perform 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. Since we will use $\F$ as our log-likelihood, Figure~\ref{fig_grid_frequency} provides an example of the space we will ask the sampler to explore. Clearly, 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 ineffecient at finding the global maxima. This problem is excabated in higher-dimensional search spaces where the volume fraction of 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 $u$, the 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? For a fully-coherent $\F$-statistic search on data containing Gaussian noise and a signal with Doppler parameters $\blambdaSignal$, the template-bank mismatch at the grid point $\blambda_{l}$ is defined to be \begin{align} \mutilde(\blambdaSignal, \blambda_{l}) \equiv 1 - \frac{\tilde{\rho}(\blambda_l;\blambdaSignal)^{2}} {\tilde{\rho}(\blambdaSignal; \blambdaSignal)^{2}}, \end{align} where $\tilde{\rho}(\blambda_l; \blambdaSignal)$ is the non-centrality parameter (c.f. Equation~\ref{eqn_twoF_expectation}) at $\blambda_l$, given that the signal is at $\blambdaSignal$. As such $\widetilde{\textrm{SNR}}(\blambdaSignal; \blambdaSignal)$ is the perfectly-matched non-centrality parameter, for which the mismatch is zero. 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 \citet{leaci2015}, this is true for the fully-coherent case only. Therefore, we will use the non-centrality parameter which easily generalised to the semi-coherent case. To make analytic calculations of the mismatch possible, as first shown by \citet{brady1998}, the mismatch can be approximated by \begin{equation} \mutilde(\blambda, \Delta\blambda) \approx \tilde{g}_{\alpha \beta}^{\phi} \Delta\lambda^{\alpha}\Delta\lambda^{\beta} + \mathcal{O}\left(\Delta\blambda^{3}\right) \end{equation} where we switch to using index notation for which we sum over repeated indices. Here, $\tilde{g}_{\alpha\beta}^{\phi}$ is the `phase-metric' given by \begin{align} \tilde{g}^{\phi}_{\alpha \beta}(\blambda) = \langle \partial_{\Delta\lambda^{\alpha}}\phi \partial_{\Delta\lambda^{\beta}}\phi \rangle - \langle \partial_{\Delta\lambda^{\alpha}}\phi \rangle \langle \partial_{\Delta\lambda^{\beta}}\phi \rangle, \label{eqn_metric} \end{align} where $\langle \cdot \rangle$ denotes the time-average over $\Tcoh$ and $\phi(t; \blambda)$ is the phase evolution of the source. The phase metric is in fact an approximation of the full metric which includes modulations of the amplitude parameters $\A$; it was shown by \citet{prix2007metric} that it is a good approximation when using data spans longer than a day and data from multiple detectors. The phase metric, Equation~\eqref{eqn_metric} provides the neccesery tool to measure distances in the Doppler parameter space in units of mismatch. To calculate it's components, we define the phase evolution of the source as \citep{wette2015} \begin{align} \phi(t; \blambda) \approx 2\pi\left( \sum_{s=0}^{\smax} f^{(s)}\frac{(t-\tref)^{s+1}}{(s+1)!} + \frac{r(t)\cdot\mathbf{n}}{c} \fmax\right), \label{eqn_phi} \end{align} where $\mathbf{n}(\alpha, \delta)$ is the fixed position of the source with respect to the solar system barycenter (with coordinates $\alpha, \delta$ the right ascension and declination), $f^(s)\equiv d^{s}\phi/dt^s$, and $\fmax$ a constant chosen conservatively to be the maximum frequency over the data span. The frequency and spin-down components of the metric can be easily calculated due to their linearity in Equation~\eqref{eqn_phi} and for the special case in which $\tref$ is in the middle of the data span, the frequency and spin-down parts of the metric are diagonal. Accurately approximating the sky components of the metric is non-trivial, but was accomplished by \citet{wette2013} for the fully-coherent case. In \citet{wette2015} it was shown how the calculate the equivalent semi-coherent metric $\hat{g}_{\alpha\beta}^{\phi}(\blambda, \Nseg)$. In the following, we will work with this calculation with the understanding that $\hat{g}_{\alpha\beta}^{\phi}(\blambda, \Nseg{=}1)= \tilde{g}_{\alpha\beta}^{\phi}(\blambda)$. To understand the volume of parameter space which a true signal would occupy, we can make use of the \emph{metric-volume} \citep{prix2007}, given by \begin{align} \mathcal{V} = \int \sqrt{\textrm{det}\hat{g}^{\phi}_{\alpha\beta}(\blambda, \Nseg)} d\blambda \approx \sqrt{\textrm{det}\hat{g}^{\phi}_{\alpha\beta}(\blambda, \Nseg)} \Delta\blambda \end{align} where in the second step we assume a constant coefficient metric. Here, $\Delta \blambda$ is the volume element which is given by \begin{equation} \Delta\lambda = \frac{\Delta\Omega}{2} %\frac{1}{2}\sin\delta\Delta\delta\Delta\alpha \prod_{s=0}^{\smax} \Delta f^{(s)}, \end{equation} where $\Delta\Omega$ is the solid angle of the sky-patch which is searched, $\Delta f^(s)$ is the extend of the frequency and spin-down range(s) searched, and the factor of $1/2$ comes from converting the normalised determinant which is computed over the whole sky to the solid angle of the directed search. \comment{Not sure I fully understand this yet, or have really derived it properly}. 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 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 determing if an MCMC search will operate in a regime where it is effecicient (i.e. where the a signal occupes 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 contributes to the total product. This is done by noticing that the metric has a particular block form: \begin{align} g_{ij} = \left[ \begin{array}{cc} g^{\rm Sky} & 0 \\ 0 & g^{\rm PE} \end{array} \right] \end{align} where $g^{\rm Sky}$ is the $2\times2$ sky-metric, while $g^{\rm PE}$ is the $(\smax{+}1){\times}(\smax{+}1)$ phase-evolution metric. As such, the volume can be decomposed as \begin{align} \mathcal{V} & = \sqrt{\textrm{det}g^{\rm Sky}}\frac{\Delta\Omega}{2} \times \sqrt{\textrm{det}g^{\rm PE}}\prod_{s=0}^{\smax}\Delta f^{(s)} \\ & = \Vsky \times \Vpe. \label{eqn_metric_volume} \end{align} Moreover, if $\tref$ is in the middle of the observation span, the diagonal nature of $g^{\rm PE}$ means that one can further identify \begin{align} \Vpe = \prod_{s=0}^{\smax}\sqrt{g^{\rm PE}_{ss}} \Delta f^{(s)} = \prod_{s=0}^{\smax}\Vpe^{(s)} \end{align} This decomposition may be useful in setting up MCMC searches. \subsection{Example: 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 $30$~Hz and a spin-down of $-1{\times}10^{-10}$~Hz/s, all other Doppler parameters are `known' and so are irrelevant. Moreover, the signal has an amplitude $h_0=10^{-24}$~Hz$^{-1/2}$ while the Gaussian noise has $\Sn=10^{-23}$~Hz$^{-1/2}$ such that the signal has a depth of 10. First, we must define a prior for each search parameter Typically, we recomend 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 Equation~\eqref{eqn_metric_volume}. For this example, we will use a uniform prior with a frequency range of $\Delta f = 10^{-7}$~Hz and a spin-down range of $\Delta \fdot=10^{-13}$~Hz/s both centered on the simulated signal frequency and spin-down rate. We set the reference time to coincide with the middle of the data span, therefore the metric volume can be decomposed into the frequency contribution and spin-down contribution: frequency, \begin{align} \Vpe^{(0)} = \frac{(\pi\Tcoh\Delta f)^2}{3} \approx 2.46 \end{align} and \begin{align} \Vpe^{(1)} = \frac{4(\pi\Delta \fdot)^2\Tcoh^{4}}{45} \approx 48.9 \end{align} such that $\V\approx120$ (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 translate them into a metric volume: for example using a Guassian 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. 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 number of walkers; this is a tuning parameter of the MCMC algorithm. The number of walkers should be typically a few hundred, the greater the number the more samples will be taken resulting in improved posterior estimates. The burn-in steps refers to an initial set of steps which are discarded as they are taken whilst the walkers converge. After they have convereged the steps are known as production steps since they are used to produce posterior estimates and calculate the marginal likelihood. Using these choices, the simulation is run. To illustrate the full MCMC process, in Figure~\ref{fig_MCMC_simple_example} we plot the progress of all the individual walkers (each represented by an individual line) as a function 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 congerging. The fact that they converge to a single unique point is due to the strength of the signal (substantially elevating the likelihood about that of Gaussian fluctuations) and the tight prior which was quantifed throug 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. \begin{figure}[htb] \centering \includegraphics[width=0.5\textwidth]{fully_coherent_search_using_MCMC_walkers} \caption{The progress of the MCMC simulation for a simulated signal in Gaussian 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.} \label{fig_MCMC_simple_example} \end{figure} \subsection{Example: noise-only} \section{Follow-up} \label{sec_follow_up} Incoherent detection statistics trade significance (the height of the peak) for sensitivity (the width of the peak). We will now discuss the advantages of using an MCMC sampler to follow-up a candidate found incoherently, increasing the coherence time until finally estimating it's parameters and significance fully-coherently. We begin by rewritting 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). \propto e^{\hat{\F}(x| \Tcoh, \blambda)} P(\blambda| \Hs I). \end{equation} Introducing the coherent time $\Tcoh$ as a variable provides an ability to adjust 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 subseuqntly incrementally increasing this coherence time in a controlled manner, aiming to allow the MCMC walkers to converge to the new likelihood before again increasing the coherence time. Ultimately, this coherence time will be increased until $\Tcoh = \Tspan$. If this is done in $\Nstages$ discreet \emph{stages}, this introduces a further set of tuning parameters, namely the ladder of coherence times $\Tcoh^{i}$, where $i \in [0, \Nstages]$ to use. In some ways, this bears a resembalance to so-called simulated annealing, a method in which the likelihood is raised to a power $1/T$ and subseuqntly `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. Of course in practise, we do not arbitarily vary $\Tcoh^i$, but rather the number of segments at each stage $\Nstages^{i}\equiv \Tspan/\Tcoh^{i}$. Ideally, the ladder of segment should be chosen 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} \mathcal{R} \equiv \frac{\V_i}{\V_{i+1}}, \end{equation} where $\mathcal{R} \ge 1$ as $\Tcoh^{i+1} > \Tcoh^{i}$. 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: \begin{align} \min_{\Nseg^{i+1} \in \mathbb{N}} \left| \log\mathcal{R} + \log\V(\Nseg^{i+1}) - \log\V(\Nseg^i)\right| \end{align} 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 itterate. 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 $\Nstages$. 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 $\V^{\min}$. \subsection{Example} We now provide an illustrative example of the follow-up method. We consider a directed search over the sky position and frequency in 100 days of data from a single detector, with $\sqrt{\Sn}=10^{-23}$~Hz$^{-1/2}$ (at the fiducial frequency of the signal). The simulated signal has an amplitude $h_0=2\times10^{-25}$ such that the signal has a depth of $\sqrt{\Sn}/h_0=50$ in the noise. First, we must define the setup for the run. Using $\mathcal{R}=10$ and $\V^{\rm min}=100$ our optimisation procudure is run and proposes the setup layed out in Table~\ref{tab_weak_signal_follow_up}. In addition, we show the number of 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 $\V^{\rm min}=100$.} \label{tab_weak_signal_follow_up} \input{weak_signal_follow_up_run_setup} \end{table} The choice of $\mathcal{R}$ and $\V^{\rm min}$ is a comprimise between the total computing time and the ability to ensure a candidate will be identified. From experimentation, we find that $\V^{\rm min}$ values of 100 or so are sufficient to ensure that any peaks are sufficiently broad during the initial stage. For $\mathcal{R}$ value much larger than $10^{3}$ or so where found to result in the MCMC simulations `loosing' the peaks between stages, we conservatively opt for 10 here, but values as large as 100 where also succesul. In Figure~\ref{fig_follow_up} we show the progress of the MCMC sampler during the follow-up. As expected from Table~\ref{tab_weak_signal_follow_up}, during the initial stage the signal peak is broad with respect to the size of the prior volume, therefore the MCMC simulation quickly converges to it. Subsequently, each time the number of segments is reduced, the peak narrows and the samplers similarly converge to this new solution. At times it can appeak 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. \begin{figure}[htb] \centering \includegraphics[width=0.5\textwidth]{weak_signal_follow_up_walkers} \caption{In the top three panels we show the progress of the 500 parallel walkers (see Figure~\ref{fig_MCMC_simple_example} for a description) during the MCMC simulation for each of the search parameters, frequency $f$, right-ascension $\alpha$ and declination $\delta$. Each vertical dashed line indicates the start of a new stage of the search, the parameters for all stages are listed in Table~\ref{tab_weak_signal_follow_up}.} \label{fig_follow_up} \end{figure} The key advantage to note here is that all walkers succefully convereged to the signal peak, which occupies $\sim 10^{-6}$ of the initial volume. While it is possible for this to occur during an ordinary MCMC simulation (with $\Tcoh$ fixed at $\Tspan$), it would take substantially longer to converge as the chains explore the other `noise peaks' in the data. \section{Alternative waveform models: transients} \label{sec_transients} \begin{figure}[htb] \centering \includegraphics[width=0.5\textwidth]{transient_search_initial_stage_twoFcumulative} \caption{} \label{fig:} \end{figure} \begin{figure}[htb] \centering \includegraphics[width=0.5\textwidth]{transient_search_corner} \caption{} \label{fig:} \end{figure} \section{Alternative waveform models: glitches} \label{sec_glitches} \section{Conclusion} \label{sec_conclusion} \section{Acknowledgements} \bibliography{bibliography} \end{document}