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The Laser Interferometer Space Antenna (LISA), due for launch in the mid2030s, is expected to observe gravitational waves (GW)s from merging massiveblack hole binaries (MBHB)s. These signals can last from days to months,depending on the masses of the black holes, and are expected to be observedwith high signal to noise ratios (SNR)s out to high redshifts. We have adaptedthe PyCBC software package to enable a template bank search and inference ofGWs from MBHBs. The pipeline is tested on the LISA data challenge (LDC)'sChallenge 2a (\enquote{Sangria}), which contains MBHBs and thousands ofgalactic binaries (GBs) in simulated instrumental LISA noise. Our searchidentifies all 6 MBHB signals with more than $92\%$ of the optimal SNR. Thesubsequent parameter inference step recovers the masses and spins within their$90\%$ confidence interval. Sky position parameters have 8 high likelihoodmodes which are recovered but often our posteriors favour the incorrect skymode. We observe that the addition of GBs biases the parameter recovery ofmasses and spins away from the injected values, reinforcing the need for aglobal fit pipeline which will simultaneously fit the parameters of the GBsignals before estimating the parameters of MBHBs.

Adapting the PyCBC pipeline to find and infer the properties of gravitational waves from massive black hole binaries in LISA