Fishing massive black hole binaries with THAMES

TL;DR

The paper introduces THAMES, a deep learning algorithm that improves detection sensitivity for gravitational waves from asymmetric intermediate-mass black hole binaries, outperforming traditional methods especially for high mass ratios.

Contribution

This work presents THAMES, a novel end-to-end deep learning-based signal detection method specifically designed for complex GW signals from IMBH binaries, demonstrating significant sensitivity gains over existing matched-filter searches.

Findings

THAMES outperforms PyCBC in detecting high mass ratio IMBH binaries.

Maximum sensitivity volume increase by a factor of 5.24 for certain mass ratios.

Deep learning effectively discriminates complex signals from noise, enhancing GW detection capabilities.

Abstract

Hierarchical mergers in a dense environment are one of the primary formation channels of intermediate-mass black hole (IMBH) binary system. We expect that the resulting massive binary system will exhibit mass asymmetry. The emitted gravitational-wave (GW) carry significant contribution from higher-order modes and hence complex waveform morphology due to superposition of different modes. Further, IMBH binaries exhibit lower merger frequency and shorter signal duration in the LIGO detector which increases the risk of them being misclassified as short-duration noisy glitches. Deep learning algorithms can be trained to discriminate noisy glitches from short GW transients. We present the $\mathtt{THAMES}$ -- a deep-learning-based end-to-end signal detection algorithm for GW signals from quasi-circular nearly edge-on, mass asymmetric IMBH binaries in advanced GW detectors. Our study shows that it outperforms matched-filter based $\mathtt{PyCBC}$ searches for higher mass asymmetric, nearly edge-on IMBH binaries. The maximum gain in the sensitive volume-time product 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 1 in 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 in volumetric sensitivity is its ability to discriminate between signals with complex waveform morphology and noisy transients, clearly demonstrating the potential of deep learning algorithms in probing into complex signal morphology in the field of gravitational wave astronomy. With the current training set, $\mathtt{THAMES}$ slightly underperforms with respect to $\mathtt{PyCBC}$-based searches targeting intermediate-mass black hole binaries with mass ratio $q \in (5, 10)$ and detector frame total mass $M_T(1+z) \in (100,200)~M_\odot$.