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

TL;DR

This work addresses the challenge of constraining CCSN explosion mechanisms through gravitational waves by exploiting frequency-specific content. It introduces a dedicated-frequency framework that follows up GW burst candidates with bandpass analyses in the LF ($f\leq 256\,\mathrm{Hz}$) and HF ($f\geq 256\,\mathrm{Hz}$) bands using a hierarchical cWB–BayesWave pipeline validated on real O3 data. By injecting five 3D CCSN waveforms with varying LF power into O3 data and measuring independent backgrounds, the study shows LF follow-ups can confirm LF content and thus favor certain explosion models, while HF follow-ups can enhance detection significance for HF-dominated events. The results indicate LF content can constrain CCSN models when present, and HF follow-ups can improve significance for high-frequency-rich triggers, offering a practical, band-specific tool for CCSN GW analysis in realistic observing conditions.

Abstract

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 (SASI) or neutrino-driven convection, and simulations show that these mechanisms emit GWs at low frequencies ($\lesssim 0.25 \,{\rm kHz}$). 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 $\leq 256 \,{\rm Hz}$, 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 SASI 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 (HF) analysis, limited to $\geq 256 \,{\rm Hz}$. The trigger has negligible power below $256 \, {\rm Hz}$, and the HF analysis successfully enhances its detection significance.

Figures (9)

• Figure 1: Schematic of GW emissions from CCSNe with slowly-rotating progenitors in the time-frequency plane. The scales on the axes are representative only. The actual durations and spectra of the GW signatures vary depending on the physical properties of the CCSN progenitor, and not all CCSNe will exhibit every signature shown.
• Figure 2: CWT time-frequency spectrograms of a sample GW signal for SFHx (left), s25 (middle) and s18 (right). The vertical axis shows the full-band analysis frequency range (32$-$2048$\,\mathrm{Hz}$) and the white horizontal lines at $256\,\mathrm{Hz}$ indicate the boundary between LF and HF components, as per the dedicated-frequency framework. The horizontal axis shows the time $t$ relative to the central time $t_0$ of the signal, and the orange vertical lines indicate $t=t_0$. The horizontal scales are the same for all three plots, and the approximate duration of the SFHx, s25 and s18 signals are 350 ms, 625 ms and 900 ms respectively. The color bar represents the linearly scaled amplitude of the signals; the minimum and maximum amplitudes in each panel correspond to the values 0 and 1 respectively. The LF energy divided by the total signal energy is quoted in the bottom right corner of each plot.
• Figure 3: O3a background measurements. The top panel shows the FAR of the background triggers as a function of the detection statistic $\eta_{\mathrm r}$, for the full-band cWB analysis. The bottom panel shows the same but for the BayesWave full-band (pink curve) and LF (purple curve) triggers, as a function of the detection statistic $\ln \mathcal{B}_{\mathcal{S}, \mathcal{G}}$. The horizontal green line at FAR $=1\,\mathrm{yr}^{-1}$ (top panel) and the vertical green line at $\ln \mathcal{B}_{\mathcal{S}, \mathcal{G}} = 0$ (bottom panel) indicate the detection thresholds for cWB and BayesWave respectively.
• Figure 4: Detection efficiency for events with FAR $\leq 1 \,\mathrm{yr}^{-1}$ versus signal amplitude $h_{\rm{rss}}$. Each point represents the empirically measured detection efficiency with ${\sim}500$ injections. The colored points represent different CCSN models as indicated by the legend, and the dashed curves in corresponding colors show the least-square fit to a cumulative log-normal distribution. The numbers in parentheses are $h_{\rm{rss}, 50}$ (in units of $\,\mathrm{Hz}^{-1/2}$), i.e. the $h_{\rm{rss}}$ value that results in 50% detection efficiency, as indicated by the horizontal dashed line.
• Figure 5: Detection efficiency for events with FAR $\leq 1 \,\mathrm{yr}^{-1}$ for the five CCSN models, based on the BayesWave follow-up analyses. The purple triangles and pink crosses show the efficiencies for the LF and full-band analyses respectively. The horizontal axis shows the average LF contribution to the overall signal energy, as reported in Section \ref{['sec:CCSN_models']}. CCSN model names are displayed next to their corresponding LF data points to aid interpretation. The gray dashed line indicates 50% detection efficiency, for reference.