9 5 10 15 20 25 30 35 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 $_{0}$ (sec) $_{τ}$1.5  (sec) 0 10 20 30 40 50 60 70 80 90 0 1 2 3 4 5 6 7 8 9 10 m$_{1}$ (solar mass) m 2  (solar mass) FIG. 3: Evolution of a swarm in the presence of a signal. The ‘ ◦ ’ and ‘ ∗ ’ markers show the pbest locations of N$_{p}$ = 81 particles when 5% and 60% of the total number of steps were completed respectively. The lines show the paths followed by the pbest locations of 5 representative particles between these two steps. With time, the pbest locations tend to congregate around the ridge-like feature produced by a signal. FIG. 2: The region in the m 1 , m 2 plane corresponding to the physically valid part of the τ 0 , τ 1 . 5 plane. The region is indicated by taking a regular grid of points in the τ 0 , τ 1 . 5 plane and mapping them to the corresponding values of m 1 , m 2 , where by convention m 1 ≥ m 2 . The ⋆ markers shows the signal locations used in the simulations. V. RESULTS WITH SIGNAL PRESENT The particles begin by moving randomly in the param- eter space but each time a particle crosses the ridge, its pbest tends to fall closer to the flanks of the ridge. As time progresses, the pbest of all particles cluster around the ridge. This increases its attractive power in the accel- eration of the particles, progressively drawing more par- ticles into exploration of the fitness function along the ridge. Fig. 3 shows snapshots of PSO at different stages in the search and the progressive clustering of pbest locations is seen clearly. A key point to note here is that no prior knowledge is built into PSO about the ridge-like feature. It is found by the particles as they explore the search region. In this section, we describe the results of simulations performed with signals added to data. We quantify the performance of PSO at four different values of signal SNR and four different locations in the τ$_{0}$ , τ$_{1}$$_{.}$$_{5}$ plane, In order to quantify the performance of PSO in the presence of a signal, we look at two figures of merit. The first is the probability of clustering defined in Sec. IV A. Since the tuning procedure requires a minimum value of 90%, the probability of clustering in the presence of a strong signal should be significantly higher but it should be consistent with the pure noise case for weak signals. When a signal is added to the data, we do not need the consistency of clustering criterion of Sec. IV A in order to confirm the association of a cluster with the global maxi- mum. Since we know the location of the signal and since the expectation of the fitness function must be maximum at that location, it suffices to check if the maximum fit- B. Figures of merit A. Qualitative changes induced by a signal It is instructive to observe how a signal affects the be- havior of the swarm. In general, the presence of the signal leads to a broadening of the peak in the fitness function. As is well known, the broadening is more pronounced in one direction, due to the correlation between estimation errors, leading to the appearance of a thin ridge-like fea- ture (c.f., Fig 1). For each combination of signal location and SNR, 50 independent data realizations are generated. The length of each realization is 64 sec, with δ$_{s}$ = 1 / 2048 sec, and the signal is added at an offset of 10 sec from the start. (The units for both τ$_{0}$ and τ$_{1}$$_{.}$$_{5}$ are in seconds). The corresponding masses (in M$_{⊙}$ ) of the binary components are, respectively, { ( m$_{1}$ = 7 . 78 , m$_{2}$ = 1 . 91) , (4 . 71 , 1 . 35) , (2 . 40 , 1 . 40) , (2 . 61 , 1 . 03) } . Fig. 2 shows the physical part of the search region mapped into the m$_{1}$ , m$_{2}$ plane along with the signal locations. SNR ∈ { 9 . 0 , 8 . 0 , 7 . 0 , 6 . 0 } , ( τ$_{0}$, τ$_{1}$$_{.}$$_{5}$ ) ∈ { (5 . 0 , 0 . 6) , (10 . 0 , 0 . 75) , (16 . 0 , 0 . 762) , (20 . 0 , 0 . 9) } .