Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.
The component masses and distances of the simulated signals are sampled from uniform distributions in the ranges [0,80]M ⊙ and [0,5000] Mpc respectively
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Normalizing flows for density estimation in multi-detector gravitational-wave searches
Normalizing flows replace binned histograms for estimating multi-detector signal parameters in PyCBC, slashing storage by three orders of magnitude with under 0.05% sensitivity loss and up to 6.55% gains in specific cases.