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Hierarchical mergers in a dense environment are one of the primary formationchannels of intermediate-mass black hole (IMBH) binary system. We expect thatthe resulting massive binary system will exhibit mass asymmetry. The emittedgravitational-wave (GW) carry significant contribution from higher-order modesand hence complex waveform morphology due to superposition of different modes.Further, IMBH binaries exhibit lower merger frequency and shorter signalduration in the LIGO detector which increases the risk of them beingmisclassified as short-duration noisy glitches. Deep learning algorithms can betrained to discriminate noisy glitches from short GW transients. We present the$\mathtt{THAMES}$ -- a deep-learning-based end-to-end signal detectionalgorithm for GW signals from quasi-circular nearly edge-on, mass asymmetricIMBH binaries in advanced GW detectors. Our study shows that it outperformsmatched-filter based $\mathtt{PyCBC}$ searches for higher mass asymmetric,nearly edge-on IMBH binaries. The maximum gain in the sensitive volume-timeproduct for mass ratio $q \in (5, 10)$ is by a factor of 5.24 (2.92) against$\mathtt{PyCBC-IMBH}$ ($\mathtt{PyCBC-HM}$) search at a false alarm rate of 1in 100 years. Compared to the broad $\mathtt{PyCBC}$ search this factor is$\sim100$ for the $q \in (10,18)$. One of the reasons for this leap involumetric sensitivity is its ability to discriminate between signals withcomplex waveform morphology and noisy transients, clearly demonstrating thepotential of deep learning algorithms in probing into complex signal morphologyin the field of gravitational wave astronomy. With the current training set,$\mathtt{THAMES}$ slightly underperforms with respect to $\mathtt{PyCBC}$-basedsearches targeting intermediate-mass black hole binaries with mass ratio $q \in(5, 10)$ and detector frame total mass $M_T(1+z) \in (100,200)~M_\odot$.

Fishing massive black hole binaries with THAMES