Speaker
Description
The emerging field of gravitational-wave astronomy opens a new window onto extreme transient sources in the Local Universe. Realizing this potential calls for model-agnostic data analysis operating at detector-limited sensitivity through advanced signal processing. We discuss Butterfly Matched Filtering (BMF), a broadband matched-filter search over dense bank of symmetrized chirp-like templates that circumvents the time-frequency uncertainty inherent to FFT-based analyses.
Applying BMF to LIGO/Virgo data, we identified a descending ravitational-wave chirp, GW170817B, following the binary neutron star merger GW170817, at 5.5σ significance from consistent and independent H1 and L1 detections. GW170817B subsequently unveiled, through GW calorimetry, a black-hole central engine powering GRB170817A. By universality of black holes, mass-scaling from this result indicates a search horizon capable of revealing the central engines of energetic core-collapse supernovae out to and beyond 160 Mpc, instead of the conventional few Mpc, with the current generation of gravitational-wave detectors — a reach comparable to that for binary neutron star mergers.
This capability opens a radically new discovery space in gravitational-wave astronomy. Implemented at exascale, these signal-processing methods are of particular interest for next-generation detectors such as the Einstein Telescope.