Dedicated-frequency analysis of gravitational-wave bursts from core-collapse supernovae with minimal assumptions

Gravitational-wave (GW) emissions from core-collapse supernovae (CCSNe) provide insights into the internal processes leading up to their explosions. Theory predicts that CCSN explosions are driven by hydrodynamical instabilities like the standing accretion shock instability or neutrino-driven convection, and simulations show that these mechanisms emit GWs at low frequencies ($\ensuremath{\lesssim}0.25\text{ }\text{ }\mathrm{kHz}$). Thus, the detection of low-frequency GWs, or lack thereof, is useful for constraining explosion mechanisms in CCSNe. This paper introduces the dedicated-frequency framework, which is designed to follow up GW burst detections using bandpass analyses. The primary aim is to study whether low-frequency (LF) follow-up analyses, limited to $\ensuremath{\le}256\text{ }\text{ }\mathrm{Hz}$, constrain CCSN explosion models in practical observing scenarios. The analysis dataset comprises waveforms from five CCSN models with different strengths of low-frequency GW emissions induced by standing accretion shock instability and/or neutrino-driven convection, injected into the Advanced LIGO data from the third observing run (O3). Eligible candidates for the LF follow-up must satisfy a benchmark detection significance and are identified using the coherent WaveBurst (cWB) algorithm. The LF follow-up analyses are performed using the BayesWave algorithm. Both cWB and BayesWave make minimal assumptions about the signal's morphology. The results suggest that the successful detection of a CCSN in the LF follow-up analysis constrains its explosion mechanism. The dedicated-frequency framework also has other applications. As a demonstration, the loudest trigger from the SN 2019fcn supernova search is followed up using a high-frequency analysis, limited to $\ensuremath{\ge}256\text{ }\text{ }\mathrm{Hz}$. The trigger has negligible power below 256 Hz, and the high-frequency analysis successfully enhances its detection significance.