Hunting for Dark Matter Clouds Around Black Hole Binaries

We develop methods for discriminating the presence of dark matter scalar clouds surrounding coalescing binary black holes via their influence on the binary's gravitational wave (GW) signal. Even after its discovery almost a century ago, we still do not know what dark matter is made of. The relatively recent direct observation of gravitational waves provides a novel way of probing dark matter. While non-rotating black holes cannot have scalar hair, nevertheless spinning black holes were shown theoretically to be able to support them. We extend the work in Choudhary et al. (Phys. Rev.D103 (2021)) to show in what sections of the binary and scalar field parameter space it will be possible to discern the deviations and measure the dark matter parameters. We also model waveform systematics with which the scalar parameters would be covariant and, therefore, difficult to distinguish. We also develop a 1D-ResNet neural network to boost the search and parameter estimation of signals from stellar-mass binaries in LIGO-Virgo-KAGRA data. We extend it to model and detect similar effects in Supermassive Binary Black Hole coalescence that LISA would target.