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tokens: weighting (red) used in Ali et al. ( 2015 ), which excises fringe rates at risk for contamination by crosstalk, and applies a Blackman–Harris window in the time domain to generate a compact time-domain convolution kernel with regions weighted below 5% of the peak response suppressed. Also illustrated are the weightings for matching the YY to the XX polarization beam (blue dashed) and for reducing polarization leakage (blue solid) described in Section 4.2 , as well as a weighting that balances the needs of sensitivity, polarization leakage, crosstalk removal, and off-axis foregrounds (green). Bottom: the time-domain convolution kernel corresponding to the red curve in the top panel. Real and imaginary components are illustrated in cyan and magenta, respectively, with the absolute amplitude illustrated in brown. The next step in implementing the fringe-rate filter is translating the analytic filter profile in fringe-rate space into a time-domain kernel that can be used to convolve the simulated time series of visibilities. In effect, we implement the fringe-rate filter as a finite impulse response (FIR) filter. The convolution kernel corresponding to this FIR filter is shown in the lower panel of Figure 3 . Applying the fringe-rate filter as an FIR filter in the time domain, as opposed to directly multiplying the desired filter to Fourier-transformed visibilities, has the advantage that flagged or missing data can be naturally excluded from the filter by neglecting FIR taps (coefficient multiplies) that target the missing data. The summed output of the FIR filter are then renormalized to account for the missing samples. Another advantage of the FIR implementation of the fringe-rate filter is the potential for windowing the time-domain filter profile. While time-domain windowing causes further deviations from the ideal fringe-rate filter profile, it can be used to produce a more compact time-domain kernel. Reducing the number of time-domain samples used in the FIR filter improves the computational efficiency of the filter, helps limit the number of samples potentially corrupted by spurious systematics such as radio frequency interference, and reduces boundary effects. In Ali et al. ( 2015 ), a 12 hr data set was filtered in this manner, with boundary effects limited to an hour on each end. The final analysis proceeded on 8.3 hr selected from within the region not affected by boundary effects. 4. APPLICATIONS In Section 3 , we discussed how a fringe-rate filter can be implemented in practice once a particular form for the filter is selected. In this section, we optimize the selection of filters (or equivalently, of fringe-rate weights) for various applications in low-frequency radio interferometry. The key to this optimization will be the insight from Section 2 , namely that the effect of fringe-rate filtering can be regarded as both a time integration and a modification of the spatial response of the primary beam on a per-baseline basis.