MBTA's SNR optimizer, live since June 2025, recovers 2-3% more SNR and produces low-latency mass/spin posteriors that match LVK's full parameter estimation for most of 71 O4a events.
Supplementing rapid Bayesian parameter estimation schemes with adaptive grids
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abstract
In the age of multi-messenger astrophysics, low-latency parameter estimation of gravitational-wave signals is essential for electromagnetic follow-up observations. In this paper, we present a new edition of the Bayesian parameter estimation scheme for compact binaries known as Rapid PE. Rapid PE parallelizes parameter estimation by fixing the intrinsic parameters (such as the masses and spins of the binary) to a grid around a search-identified intrinsic point and by integrating over the extrinsic parameters via Monte Carlo sampling. The original version of Rapid PE calculated an effective Fisher matrix to fix the intrinsic parameters to an ellipsoidal grid. Here we use rectilinear gridding in intrinsic space. The use of rectilinear intrinsic grids provides the option to more easily include spin and allows for adaptive grid refinements to mitigate search biases and map out the posterior more completely without sacrificing sampling efficiency. We demonstrate that this parameter estimation method produces reliable results for simulated gravitational-wave signals from binary neutron star mergers.
fields
gr-qc 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Enhancing online estimation of CBC parameters with the low-latency MBTA analysis
MBTA's SNR optimizer, live since June 2025, recovers 2-3% more SNR and produces low-latency mass/spin posteriors that match LVK's full parameter estimation for most of 71 O4a events.