Speaker
Description
We introduce a framework based on Short-time Fourier Transforms (SFTs)
to analyze long-duration gravitational wave signals from compact binaries.
Targeted systems include binary neutron stars observed by third-generation
ground-based detectors and massive black-hole binaries observed by the LISA
space mission, for which we present a pilot application. Leveraging differentiable
and GPU-parallelizable gravitational-wave models, ours is an extremely fast,
scalable, and parallelizable implementation of the gravitational-wave inner product,
the key building block of all gravitational-wave data treatments. Overall,
we achieve computing cost reduction in evaluating an inner product of three to
five orders of magnitude, depending on the specific application, with respect to a standard approach.
By speeding up this low-level element so massively, SFTs and differentiable GPU-accelerated
waveform models provide an extremely promising solution for current and future gravitational-wave
data-analysis problems.
| AI keywords | differentiable programming; GPUs; low-latency; |
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