Speaker
Description
The detection of gravitational waves (GWs) from compact binary mergers has become routine. However, stochastic and non-stationary signals, such as those expected from core-collapse supernovae (CCSNe), remain elusive. These complex, burst-like signals pose a significant challenge for traditional Bayesian inference methods, which can be computationally expensive and struggle with intractable likelihoods. In this talk, we explore simulation-based inference (SBI) as a potential alternative for analyzing such challenging GW transients. We review the application of a traditional sampler to a simulated CCSNe signal, demonstrating its performance on this test case, and discuss preliminary insights into how a modern SBI approach might address key limitations. This work underscores the promise of SBI for enabling efficient inference on stochastic GW sources as we approach the era of next-generation detectors.
