Pith. sign in

REVIEW 3 major objections 5 minor 81 references

Waveform Reconstruction of Core-Collapse Supernova Gravitational Waves with Improved Multisynchrosqueezing Transform

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that the improved multisynchrosqueezing transform (IMSST) reconstructs simulated core-collapse supernova gravitational-wave waveforms at a match threshold of 0.75 out to about 317 kpc with the Einstein Telescope and 37…

desk verdict The IMSST-vs-STFT comparison is undefined: Eq. (14) makes the two reconstructions identical, so the headline distances are unsupported. read the letter →

arxiv 2412.05962 v1 pith:HE3YSI2O submitted 2024-12-08 astro-ph.HE

Yong Yuan , Ao-Ran Wang , Zhuo-Tao Li , Gang Yu , Hou-Jun Lü , Peng Xu , Xi-Long Fan This is my paper · ORCID
classification astro-ph.HE
keywords core-collapsesupernovaegravitationalwavedataanalysistime-frequencyimprovedmultisynchrosqueezingtransformwaveformreconstructionEinsteinTelescopeadvancedLIGOmatchscore
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Core-collapse supernova gravitational waves are too stochastic for matched-filter searches, so this paper asks whether a high-resolution time-frequency transform can pull their waveforms out of noisy detector data directly. Its central claim is that the improved multisynchrosqueezing transform (IMSST) can: in simulated advanced LIGO and Einstein Telescope data, the method recovers injected supernova waveforms with match score at least 0.75 out to about 37 kpc and 317 kpc, respectively, and under the Einstein Telescope simulation it reaches 317 kpc versus 186 kpc for the standard short-time Fourier transform. The paper also reports false-alarm probabilities of reconstruction as low as $6.2 \times 10^{-3}$ for amplitude-normalized aLIGO data and $1.5 \times 10^{-2}$ for normalized ET data at that threshold. If those numbers hold, a sharper time-frequency picture would be a practical waveform-extraction tool for supernova searches, not just a visualization aid.

What carries the argument

The central object is the IMSST time-frequency representation $T_s^{[N]}(\tau,\eta)$, built from the STFT $G(\tau,\omega)$ by the iterative synchrosqueezing map $\hat{\omega}_R^{[N]}(\tau,\omega)=\mathrm{Round}(\mathrm{Round}(2\hat{\omega}^{[N]}(\tau,\omega))/2)$, which reassigns each coefficient to a sharply located frequency. The reconstruction identity is Eq. (14), $s(\tau)=(2\pi g(0))^{-1}\int T_s^{[N]}(\tau,\omega)\,d\omega$, which carries the argument by turning the time-frequency plane back into a waveform. The quality metric is the match score $\eta$ of Eq. (16), with threshold 0.75, and the false-alarm probability of reconstruction comes from the noise-only match-score distribution. The sharper time-frequency concentration is what the paper credits for the improved match scores.

What would settle it

Recompute the ET match-score curves exactly as described in the method section: if the reconstructed waveform is the full-frequency integral of the IMSST plane from Eq. (14), the curves must coincide with STFT's, because that integral equals $(2\pi g(0))^{-1}\int G(\tau,\omega)\,d\omega$. Any difference between IMSST and STFT therefore depends on a coefficient-selection rule that the paper does not specify; stating that rule and showing that it changes the match scores would settle the claim.

Watch

Extended reading notes

Core claim

The paper's discovery claim is that IMSST—a time-frequency tool that sharpens the short-time Fourier transform by iteratively reassigning spectral energy onto instantaneous-frequency curves with a rounding operation—can reconstruct simulated CCSN gravitational-wave waveforms from single-detector data. Using a library of magnetorotational and neutrino-driven waveforms spanning $9$–$60\,M_\odot$, injected into aLIGO and ET noise and then whitened and bandpass-filtered, the authors measure agreement through the match score and take 0.75 as the success threshold. They find maximum reconstructable distances of about 37 kpc for aLIGO and 317 kpc for ET with IMSST, versus 186 kpc for STFT under the ET simulation. They report false-alarm probabilities at threshold: $2.1\times10^{-2}$ and $6.2\times10^{-3}$ for aLIGO at 10 kpc, and $1.3\times10^{-1}$ and $1.5\times10^{-2}$ for ET at 100 kpc, before and after amplitude normalization to $5\times10^{-21}$. The authors conclude that IMSST reconstructs CCSN waveforms more effectively than STFT, that reconstruction quality is driven by signal amplitude rather than explosion mechanism, and that the false-alarm/reconstruction-rate trade-off must be balanced in practice.

Load-bearing premise

The load-bearing premise is that the IMSST reconstruction used to produce the match scores is not the full-frequency integral of Eq. (14)—which is mathematically the same as the STFT reconstruction—but includes an additional, unstated step of choosing which time-frequency coefficients to keep; without that selection rule the reported 317 kpc versus 186 kpc comparison is not defined and not reproducible.

Editorial extensions

If this is right

  • At the 0.75 match threshold, IMSST would give ET a CCSN waveform-reconstruction reach of about 317 kpc, roughly 1.7 times the 186 kpc reach claimed for the same STFT-based reconstruction.
  • The reported false-alarm probabilities (as low as $6.2\times10^{-3}$ for normalized aLIGO data and $1.5\times10^{-2}$ for normalized ET data) imply that, under the paper's noise model, threshold-passing reconstructions are unlikely to be pure-noise artifacts.
  • Because match scores track signal amplitude rather than the magnetorotational or neutrino-driven mechanism, the method would recover waveforms without distinguishing the explosion mechanism, and match score alone cannot classify CCSN types.
  • Amplitude normalization to $5\times10^{-21}$ lowers FAPR and raises the reconstruction event rate for both detectors, indicating that recoverability is governed mainly by detected strain rather than waveform morphology.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because Eq. (14) integrates the whole time-frequency plane and reduces to the STFT reconstruction, the reported IMSST-vs-STFT gap requires an unstated coefficient-selection step (for example, keeping only ridge or band coefficients); making that selection rule explicit would let other groups reproduce the 317 kpc reach.
  • A natural control experiment would apply the same selection rule to plain STFT coefficients, separating any gain from the sharper representation from gain due to discarding noise-dominated coefficients.
  • The FAPR framework could be turned into a detection statistic by scanning thresholds and sky positions, giving a per-trial false-alarm rate rather than fixed-location values.
  • Extending the single-detector reconstruction to a detector network with coincidence checks would likely lower FAPR and give a more realistic estimate of the distances at which CCSN waveforms could actually be recovered.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper applies the improved multisynchrosqueezing transform (IMSST) to reconstruct simulated core-collapse supernova gravitational-wave signals in aLIGO and Einstein Telescope noise, using the match score to quantify reconstruction quality and a false-alarm probability of reconstruction (FAPR) to assess whether reconstructed waveforms could arise from noise. The headline claims are that IMSST achieves maximum reconstruction distances of about 37 kpc for aLIGO and 317 kpc for ET at a match threshold of 0.75, and that IMSST outperforms STFT, whose ET limit is reported as 186 kpc. The paper also reports FAPR values for a library of 2096 CCSN waveforms at 10 kpc and 100 kpc, with and without amplitude normalization.

Significance. If the central quantitative claims were supported, the paper would provide evidence that a higher-resolution time-frequency representation can improve waveform extraction from noisy CCSN data. The paper has notable strengths: it uses a broad catalog of magnetorotational and neutrino-driven CCSN waveforms, applies whitening and bandpass preprocessing, defines a concrete match-score criterion, and estimates FAPR from independent noise-only realizations. However, the central comparison between IMSST and STFT is undermined by the paper's own reconstruction equation: the full-frequency integral of the IMSST representation is exactly the STFT reconstruction, so the reported 317 kpc versus 186 kpc difference requires an unstated coefficient-selection rule that never appears in the manuscript. The FAPR values are also internally inconsistent across sections, and the dataset accounting is arithmetically wrong. These are load-bearing defects that make the numerical results unverifiable and the central claim unsupported as written.

major comments (3)
  1. [Section 2.2, Eqs. (12)-(14)] The IMSST reconstruction formula in Eq. (14) integrates Ts[N] over all frequencies. Substituting Eq. (12) into Eq. (14) gives (2πg(0))^{-1} ∫∫ G(τ,ω) δ(η-ω_R^[N](τ,ω)) dω dη = (2πg(0))^{-1} ∫ G(τ,ω) dω, which is exactly the STFT reconstruction in Eq. (3) for the same window. Therefore, for identical input data and window, the full-band IMSST and STFT reconstructions are numerically identical, and the 317 kpc versus 186 kpc difference reported in Section 3.4 and Fig. 5 cannot arise from the stated method. The paper does not specify any post-processing selection rule, such as ridge extraction, frequency-band restriction, masking, or thresholding before inversion. Without such a rule the comparison is undefined, and with an unstated rule the result is not reproducible.
  2. [Sections 3.3 and 3.4 versus Fig. 6 caption and Section 4] The FAPR values for the ET, 100 kpc, unnormalized simulation are inconsistent across the manuscript. Section 3.3 gives 1.3e-1 for IMSST; Section 3.4 text gives 2.1e-2 for IMSST and 1.4e-2 for STFT; the Fig. 6 caption and Section 4 state 1.3e-1 and 5.6e-2 for IMSST and STFT, respectively. Since FAPR is a central quantitative result of the paper, the text must report one auditable set of values; the current disagreement prevents verification of the false-alarm claims.
  3. [Section 3.3, dataset accounting] The dataset accounting is inconsistent: 2096 waveforms injected twice gives 4192 simulated datasets, not 4170 as stated. The same 4170 count is then used for the pure-noise datasets and for the ET runs. This arithmetic discrepancy, together with the FAPR inconsistencies above, prevents an auditor from reconstructing the numerical analysis from the text.
minor comments (5)
  1. [Eq. (9)] The phase model s(t) = A(t)e^{i(φ(t)+φ'(t)(u-t)+0.5φ''(u-t)^2)} uses an undefined variable u and appears to be missing a closing parenthesis around the quadratic term; please clarify the notation and correct the expression.
  2. [Eq. (11)] The expression for G(τ,ω) appears to contain a typographical issue in the exponent, with braces missing around the denominator; the derivation from Eq. (9) should be shown explicitly.
  3. [Section 2.2] The notation for the reassigned frequency is inconsistent: Eq. (12) uses ω_R^[N] while the surrounding text uses ω̂_R^[N]; please use a single symbol throughout.
  4. [Figures 4 and 6 captions] The figure captions refer to 'FAR' while the text defines and uses 'FAPR'; please standardize the terminology.
  5. [Data Availability] The Data Availability statement lists only the waveform sources; providing the analysis code and the specific IMSST parameter choices (window width, iteration number, bandpass design) would be necessary for reproducibility, especially given the missing reconstruction-selection rule.

Circularity Check

1 steps flagged · score 6.0 of 10

The IMSST reconstruction in Eq. (14) collapses by construction to the STFT reconstruction in Eq. (3), so the reported 317 kpc vs 186 kpc advantage is not derivable from the stated method.

  1. self definitional [Section 2.2, Eqs. (12)-(14) vs Section 2.1, Eq. (3); used in Section 3.4, Fig. 5]
    "+∞ −∞ Ts[N](τ, η)dη = +∞ −∞ +∞ −∞ G(τ, ω)δ(η − ˆω[N] R (τ, ω))dωdη = ... = (2πg(0))s(τ). (13) Thus, the reconstructed GW signal can be expressed as: s(τ) = (2πg(0))−1 +∞ −∞ Ts[N](τ, ω)dω. (14) ... Therefore, the original signal s(t) can be reconstructed by s(τ) = 1 2πg(0) +∞ −∞ G(τ, ω)dω. (3)"

    Substituting Eq. (12) into Eq. (14) makes the η-integral of the reassignment delta collapse to unity, leaving (2πg(0))^{-1}∫G(τ,ω)dω, which is exactly the STFT reconstruction Eq. (3) with the same window and prefactor. Thus, for identical input and window, the IMSST waveform defined by Eq. (14) is numerically identical to the STFT waveform. The paper's headline comparison — IMSST reaching 317 kpc versus STFT 186 kpc — therefore cannot follow from the equations as written; any such difference requires an unstated coefficient-selection or masking step before inversion, which is never specified. The claimed 'improvement' is thus not a derived consequence of the method but a byproduct of an undefined post-processing choice.

full rationale

The central derivation chain is internally circular/undefined: the IMSST reconstruction formula (14) is, by the paper's own algebra (13), identical to the STFT reconstruction (3). Consequently the two match-score curves in Fig. 5 should coincide, and the claimed 317 kpc vs 186 kpc maximum-reconstruction distances cannot be attributed to the TFA methods as described. This is load-bearing because the abstract and Section 3.4 present exactly this distance advantage as the main result. The FAPR analysis itself is not circular: it uses independent noise-only realizations, so the false-alarm probabilities provide external evidence even though some quoted FAPR values are inconsistent between the text and figure captions (a correctness concern, not circularity). The 0.75 match threshold is adopted from the authors' own prior work (Yuan et al. 2024), a minor self-citation, but because it is applied identically to both methods it does not by itself force the IMSST/STFT distance comparison. Overall score 6: one central 'prediction' reduces by construction to an identity, though the benchmark data are external and no parameter is fitted to the target distances.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physics entities, but its headline numbers depend on several unstated signal-processing choices (window width, iteration count, frequency selection), on a prior-paper threshold, and on the representativeness of simulation waveforms.

free parameters (5)
  • Gaussian window standard deviation σ
    Appears in Eq. (10); controls time-frequency resolution for both IMSST and STFT; numerical value is never given, though it directly affects reconstruction accuracy and the match scores.
  • IMSST iteration number N
    Appears in Eqs. (7)-(9); number of synchrosqueezing iterations used in all runs is unspecified, and it determines the sharpness of the time-frequency representation.
  • Reconstruction frequency range/selection
    The paper writes a full-frequency integral (Eq. 14) but the numerical results require an unstated band or ridge selection; this is the key parameter separating IMSST from STFT.
  • Match score threshold = 0.75
    Adopted from the authors' prior work (Yuan et al. 2024); all maximum-distance and FAPR claims depend on this value, which is not re-derived.
  • Bandpass filter band = [20, 2000] Hz
    Whitening and bandpass filtering are applied before reconstruction; bounds are given but filter design is not.
assumptions (5)
  • standard math The GW signal can be modeled as a complex analytic signal s(t)=A(t)e^{iφ(t)} with slowly varying amplitude and instantaneous frequency.
    Used in Section 2.2 to derive the STFT and IMSST instantaneous frequency estimates. For a real GW strain, this requires forming an analytic signal; this step is not explicitly discussed.
  • ad hoc to paper The phase evolution is locally quadratic, s(t)=A(t)e^{i(φ+φ'(u-t)+0.5φ''(u-t)^2)}, with a Gaussian window.
    Eq. (9) is the approximation that yields the closed-form STFT in Eq. (11). It is not validated for the supernova waveforms.
  • domain assumption The simulated waveforms in Table 1 are representative of real CCSN GW signals.
    The entire analysis is based on a catalog of public 2D and 3D simulation waveforms; if these models are not representative, the reconstruction distances do not transfer to real data.
  • domain assumption Whitening and bandpass filtering convert colored detector noise into Gaussian white noise, so the unweighted inner product in the match score is valid.
    Stated in the footnote of Section 3.2; if the residual noise is not white, the match score does not have the claimed statistical meaning.
  • domain assumption Antenna pattern functions are constant over the roughly 1 s signal duration.
    Adopted in Section 3.1; valid for a fixed sky location but ignores Earth rotation effects over longer signals.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Waveform Reconstruction of Core-Collapse Supernova Gravitational Waves with Improved Multisynchrosqueezing Transform." pith.science (2026). https://pith.science/paper/HE3YSI2O

@misc{pith2026241205962,
  author       = {Pith},
  title        = {Pith review of: Waveform Reconstruction of Core-Collapse Supernova Gravitational Waves with Improved Multisynchrosqueezing Transform},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HE3YSI2O}},
  note         = {Machine review of arXiv:2412.05962}
}
abstract

Gravitational waves (GWs) from core-collapse supernovae (CCSNe) have been proposed as a means to probe the internal physical properties of supernovae. However, due to their complex time-frequency structure, effectively searching for and extracting GW signals from CCSNe remains an unsolved challenge. In this paper, we apply the improved multisynchrosqueezing transform (IMSST) method to reconstruct simulated GW data based on the advanced LIGO (aLIGO) and Einstein Telescope (ET) detectors. These data are generated by the magnetorotational and neutrino-driven mechanisms, and we use the match score as the criterion for evaluating the quality of the reconstruction. To assess whether the reconstructed waveforms correspond to true GW signals, we calculate the false alarm probability of reconstruction (FAPR). For GW sources located at 10 kpc and datasets where the waveform amplitudes are normalized to $5 \times 10^{-21}$ observed by aLIGO, FAPR are $2.1 \times 10^{-2}$ and $6.2 \times 10^{-3}$, respectively. For GW sources at 100 kpc and with waveform amplitudes normalized to $5 \times 10^{-21}$ observed by ET, FAPR are $1.3 \times 10^{-1}$ and $1.5 \times 10^{-2}$, respectively. When the gravitational wave strain reaches $7 \times 10^{-21}$ and the match score threshold is set to 0.75, the IMSST method achieves maximum reconstruction distances of approximately 37 kpc and 317 kpc for aLIGO and ET, respectively. Finally, we compared the performance of IMSST and STFT in waveform reconstruction based on the ET. The results show that the maximum reconstructable distance using STFT is 186 kpc.

Figures

Figures reproduced from arXiv: 2412.05962 by the authors.

Figure 1
Figure 1. Results of the waveform reconstruction at a distance of 10 kpc using the IMSST method for four GW signals. In each panel, the top graph represents the whitened and filtered injected waveform (black line) and the reconstructed waveform (red line), the bottom graph represents the residual between the whitened and filtered injected waveform and the reconstructed waveform (green line). 0 50 100 150 200 250 300 350 D [kp… view at source ↗
Figure 2
Figure 2. The evolution of the match score with distance. The IMSST analysis results based on the simulated data from ET and aLIGO detectors are rep￾resented by solid and dashed lines, respectively. The four colors correspond to four different GW waveforms: red for M1, yellow for M2, green for N1, and blue for N2. The purple horizontal line represents the threshold, which is 0.75. observed that, regardless of whether the ET o… view at source ↗
Figure 3
Figure 3. Histogram of match scores for the simulated data based on aLIGO detector at 10 kpc, illustrating the distributions with waveform injection (solid-colored bars) and without waveform injection (hatched bars). The in￾jected waveforms include M1, M2, N1, and N2, each occurring with equal frequency. The purple vertical line indicates the threshold of 0.75. the M1 waveform. Compared to the other three waveforms, the M1 wa… view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: The evolution of the match score with distance for the two time￾frequency analysis methods. The IMSST and STFT analysis results based on the simulated data from the ET detector are represented by solid and dashed lines, respectively. The four colors correspond to four …
Figure 4
Figure 4. Figure 4: The histograms of the match scores for the simulated datasets based on the aLIGO and ET detectors, including the distributions for match scores with waveform injection (blue) and without waveform injection (purple). The injected waveforms consist of the 2096 waveforms …
Figure 6
Figure 6. Figure 6: The panels from the first to the fourth row are similar to those in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

81 extracted references · 29 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  3. [3]

    Abadie J., et al., 2012, @doi [ ] 10.1103/PhysRevD.85.122007 , https://ui.adsabs.harvard.edu/abs/2012PhRvD..85l2007A 85, 122007

    work page doi:10.1103/physrevd.85.122007 2012

  4. [4]

    P., et al., 2016a, @doi [ ] 10.1103/PhysRevD.94.102001 , https://ui.adsabs.harvard.edu/abs/2016PhRvD..94j2001A 94, 102001

    Abbott B. P., et al., 2016a, @doi [ ] 10.1103/PhysRevD.94.102001 , https://ui.adsabs.harvard.edu/abs/2016PhRvD..94j2001A 94, 102001

    work page doi:10.1103/physrevd.94.102001

  5. [5]

    P., et al., 2016b, @doi [ ] 10.1103/PhysRevLett.116.061102 , https://ui.adsabs.harvard.edu/abs/2016PhRvL.116f1102A 116, 061102

    Abbott B. P., et al., 2016b, @doi [ ] 10.1103/PhysRevLett.116.061102 , https://ui.adsabs.harvard.edu/abs/2016PhRvL.116f1102A 116, 061102

    doi:10.1103/physrevlett.116.061102

  6. [6]

    P., et al., 2017a, @doi [Classical and Quantum Gravity] 10.1088/1361-6382/aa51f4 , https://ui.adsabs.harvard.edu/abs/2017CQGra..34d4001A 34, 044001

    Abbott B. P., et al., 2017a, @doi [Classical and Quantum Gravity] 10.1088/1361-6382/aa51f4 , https://ui.adsabs.harvard.edu/abs/2017CQGra..34d4001A 34, 044001

    doi:10.1088/1361-6382/aa51f4

  7. [7]

    P., et al., 2017b, @doi [ ] 10.1103/PhysRevD.95.042003 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95d2003A 95, 042003

    Abbott B. P., et al., 2017b, @doi [ ] 10.1103/PhysRevD.95.042003 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95d2003A 95, 042003

    work page doi:10.1103/physrevd.95.042003

  8. [8]

    P., et al., 2019, @doi [ ] 10.1103/PhysRevD.100.024017 , https://ui.adsabs.harvard.edu/abs/2019PhRvD.100b4017A 100, 024017

    Abbott B. P., et al., 2019, @doi [ ] 10.1103/PhysRevD.100.024017 , https://ui.adsabs.harvard.edu/abs/2019PhRvD.100b4017A 100, 024017

    doi:10.1103/physrevd.100.024017 2019

Show all 81 references
  1. [9]

    P., et al., 2020a, @doi [Living Reviews in Relativity] 10.1007/s41114-020-00026-9 , https://ui.adsabs.harvard.edu/abs/2020LRR....23....3A 23, 3

    Abbott B. P., et al., 2020a, @doi [Living Reviews in Relativity] 10.1007/s41114-020-00026-9 , https://ui.adsabs.harvard.edu/abs/2020LRR....23....3A 23, 3

    doi

  2. [10]

    P., et al., 2020b, @doi [ ] 10.1103/PhysRevD.101.084002 , https://ui.adsabs.harvard.edu/abs/2020PhRvD.101h4002A 101, 084002

    Abbott B. P., et al., 2020b, @doi [ ] 10.1103/PhysRevD.101.084002 , https://ui.adsabs.harvard.edu/abs/2020PhRvD.101h4002A 101, 084002

    doi

  3. [11]

    Abbott R., et al., 2021, @doi [ ] 10.1103/PhysRevD.104.122004 , https://ui.adsabs.harvard.edu/abs/2021PhRvD.104l2004A 104, 122004

    2021 doi

  4. [12]

    Abbott R., et al., 2022, @doi [Progress of Theoretical and Experimental Physics] 10.1093/ptep/ptac073 , https://ui.adsabs.harvard.edu/abs/2022PTEP.2022f3F01A 2022, 063F01

    2022 doi

  5. [13]

    Abbott R., et al., 2023, @doi [Physical Review X] 10.1103/PhysRevX.13.041039 , https://ui.adsabs.harvard.edu/abs/2023PhRvX..13d1039A 13, 041039

    2023 doi

  6. [14]

    M., Ott C

    Abdikamalov E., Gossan S., DeMaio A. M., Ott C. D., 2014, @doi [ ] 10.1103/PhysRevD.90.044001 , https://ui.adsabs.harvard.edu/abs/2014PhRvD..90d4001A 90, 044001

    2014 doi

  7. [15]

    Acernese F., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/2/024001 , https://ui.adsabs.harvard.edu/abs/2015CQGra..32b4001A 32, 024001

    2015 doi

  8. [16]

    Ackley K., et al., 2020, @doi [ ] 10.1017/pasa.2020.39 , https://ui.adsabs.harvard.edu/abs/2020PASA...37...47A 37, e047

    2020 doi

  9. [17]

    arXiv:2411.12524

    Andresen H., Finkel B., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2411.12524 , https://ui.adsabs.harvard.edu/abs/2024arXiv241112524A p. arXiv:2411.12524

    2024 doi arXiv

  10. [18]

    u ller B., M \

    Andresen H., M \"u ller B., M \"u ller E., Janka H. T., 2017, @doi [ ] 10.1093/mnras/stx618 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.2032A 468, 2032

    2017 doi

  11. [19]

    T., Summa A., Gill K., Zanolin M., 2019, @doi [ ] 10.1093/mnras/stz990 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2238A 486, 2238

    Andresen H., M \"u ller E., Janka H. T., Summa A., Gill K., Zanolin M., 2019, @doi [ ] 10.1093/mnras/stz990 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.486.2238A 486, 2238

    2019 doi

  12. [20]

    Auger F., Flandrin P., 1995, IEEE Transactions on signal processing, 43, 1068

    1995

  13. [21]

    Auger F., Flandrin P., Lin Y.-T., McLaughlin S., Meignen S., Oberlin T., Wu H.-T., 2013, IEEE Signal Processing Magazine, 30, 32

    2013

  14. [22]

    Baron E., Cooperstein J., 1990, @doi [ ] 10.1086/168649 , https://ui.adsabs.harvard.edu/abs/1990ApJ...353..597B 353, 597

    1990 doi

  15. [23]

    A., 1990, @doi [Reviews of Modern Physics] 10.1103/RevModPhys.62.801 , https://ui.adsabs.harvard.edu/abs/1990RvMP...62..801B 62, 801

    Bethe H. A., 1990, @doi [Reviews of Modern Physics] 10.1103/RevModPhys.62.801 , https://ui.adsabs.harvard.edu/abs/1990RvMP...62..801B 62, 801

    1990 doi

  16. [24]

    A., Wilson J

    Bethe H. A., Wilson J. R., 1985, @doi [ ] 10.1086/163343 , https://ui.adsabs.harvard.edu/abs/1985ApJ...295...14B 295, 14

    1985 doi

  17. [25]

    M., Mezzacappa A., DeMarino C., 2003, @doi [ ] 10.1086/345812 , https://ui.adsabs.harvard.edu/abs/2003ApJ...584..971B 584, 971

    Blondin J. M., Mezzacappa A., DeMarino C., 2003, @doi [ ] 10.1086/345812 , https://ui.adsabs.harvard.edu/abs/2003ApJ...584..971B 584, 971

    2003 doi

  18. [26]

    Burrows A., Vartanyan D., 2021, @doi [ ] 10.1038/s41586-020-03059-w , https://ui.adsabs.harvard.edu/abs/2021Natur.589...29B 589, 29

    2021 doi

  19. [27]

    L., Heng I

    Chan M. L., Heng I. S., Messenger C., 2020, @doi [ ] 10.1103/PhysRevD.102.043022 , https://ui.adsabs.harvard.edu/abs/2020PhRvD.102d3022C 102, 043022

    2020 doi

  20. [28]

    Daubechies I., Lu J., Wu H.-T., 2011, Applied and computational harmonic analysis, 30, 243

    2011

  21. [29]

    D., Marek A., Janka H

    Dimmelmeier H., Ott C. D., Marek A., Janka H. T., 2008, @doi [ ] 10.1103/PhysRevD.78.064056 , https://ui.adsabs.harvard.edu/abs/2008PhRvD..78f4056D 78, 064056

    2008 doi

  22. [30]

    Gabor D., 1946, Journal of the Institution of Electrical Engineers-part III: radio and communication engineering, 93, 429

    1946

  23. [31]

    E., Sutton P., Stuver A., Zanolin M., Gill K., Ott C

    Gossan S. E., Sutton P., Stuver A., Zanolin M., Gill K., Ott C. D., 2016, @doi [ ] 10.1103/PhysRevD.93.042002 , https://ui.adsabs.harvard.edu/abs/2016PhRvD..93d2002G 93, 042002

    2016 doi

  24. [32]

    Hayama K., Kuroda T., Kotake K., Takiwaki T., 2015, @doi [ ] 10.1103/PhysRevD.92.122001 , https://ui.adsabs.harvard.edu/abs/2015PhRvD..92l2001H 92, 122001

    2015 doi

  25. [33]

    S., 2009, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/26/10/105005 , https://ui.adsabs.harvard.edu/abs/2009CQGra..26j5005H 26, 105005

    Heng I. S., 2009, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/26/10/105005 , https://ui.adsabs.harvard.edu/abs/2009CQGra..26j5005H 26, 105005

    2009 doi

  26. [34]

    Hild S., et al., 2011, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/28/9/094013 , https://ui.adsabs.harvard.edu/abs/2011CQGra..28i4013H 28, 094013

    2011 doi

  27. [35]

    C.-C., Pan K.-C., Li K.-L., Yen C.-C., Kong A

    Hu C.-P., Lin L. C.-C., Pan K.-C., Li K.-L., Yen C.-C., Kong A. K. H., Hui C. Y., 2022, @doi [ ] 10.3847/1538-4357/ac8165 , https://ui.adsabs.harvard.edu/abs/2022ApJ...935..127H 935, 127

    2022 doi

  28. [36]

    Janka H.-T., 2012, @doi [Annual Review of Nuclear and Particle Science] 10.1146/annurev-nucl-102711-094901 , https://ui.adsabs.harvard.edu/abs/2012ARNPS..62..407J 62, 407

    2012 doi

  29. [37]

    T., Langanke K., Marek A., Mart \' nez-Pinedo G., M \"u ller B., 2007, @doi [ ] 10.1016/j.physrep.2007.02.002 , https://ui.adsabs.harvard.edu/abs/2007PhR...442...38J 442, 38

    Janka H. T., Langanke K., Marek A., Mart \' nez-Pinedo G., M \"u ller B., 2007, @doi [ ] 10.1016/j.physrep.2007.02.002 , https://ui.adsabs.harvard.edu/abs/2007PhR...442...38J 442, 38

    2007 doi

  30. [38]

    Janka H.-T., Melson T., Summa A., 2016, @doi [Annual Review of Nuclear and Particle Science] 10.1146/annurev-nucl-102115-044747 , https://ui.adsabs.harvard.edu/abs/2016ARNPS..66..341J 66, 341

    2016 doi

  31. [39]

    Klimenko S., et al., 2016, @doi [ ] 10.1103/PhysRevD.93.042004 , https://ui.adsabs.harvard.edu/abs/2016PhRvD..93d2004K 93, 042004

    2016 doi

  32. [40]

    Kotake K., Sato K., Takahashi K., 2006, @doi [Reports on Progress in Physics] 10.1088/0034-4885/69/4/R03 , https://ui.adsabs.harvard.edu/abs/2006RPPh...69..971K 69, 971

    2006 doi

  33. [41]

    Kotake K., Sumiyoshi K., Yamada S., Takiwaki T., Kuroda T., Suwa Y., Nagakura H., 2012, @doi [Progress of Theoretical and Experimental Physics] 10.1093/ptep/pts009 , https://ui.adsabs.harvard.edu/abs/2012PTEP.2012aA301K 2012, 01A301

    2012 doi

  34. [42]

    Kuroda T., Kotake K., Takiwaki T., 2016, @doi [ ] 10.3847/2041-8205/829/1/L14 , https://ui.adsabs.harvard.edu/abs/2016ApJ...829L..14K 829, L14

    2016 doi

  35. [43]

    Kuroda T., Kotake K., Hayama K., Takiwaki T., 2017, @doi [ ] 10.3847/1538-4357/aa988d , https://ui.adsabs.harvard.edu/abs/2017ApJ...851...62K 851, 62

    2017 doi

  36. [44]

    LIGO Scientific Collaboration et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/7/074001 , https://ui.adsabs.harvard.edu/abs/2015CQGra..32g4001L 32, 074001

    2015 doi

  37. [45]

    Maggiore M., et al., 2020, @doi [ ] 10.1088/1475-7516/2020/03/050 , https://ui.adsabs.harvard.edu/abs/2020JCAP...03..050M 2020, 050

    2020 doi

  38. [46]

    L., 2015, PhD thesis, University of Massachusetts Amherst, @doi 10.7275/7537749.0

    McIver J. L., 2015, PhD thesis, University of Massachusetts Amherst, @doi 10.7275/7537749.0

    2015 doi

  39. [47]

    arXiv:2401.11635

    Mezzacappa A., Zanolin M., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2401.11635 , https://ui.adsabs.harvard.edu/abs/2024arXiv240111635M p. arXiv:2401.11635

    2024 doi arXiv

  40. [48]

    V., Audit E., Zank G

    Mezzacappa A., et al., 2014, in Pogorelov N. V., Audit E., Zank G. P., eds, Astronomical Society of the Pacific Conference Series Vol. 488, 8th International Conference of Numerical Modeling of Space Plasma Flows (ASTRONUM 2013). p. 102 ( @eprint arXiv 1405.7075 ), @doi 10.485...

    2014 doi arXiv

  41. [49]

    S., Shukirgaliyev B., Abdikamalov E., 2024, @doi [ ] 10.1093/mnras/stae714 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3582M 529, 3582

    Mitra A., Orel D., Abylkairov Y. S., Shukirgaliyev B., Abdikamalov E., 2024, @doi [ ] 10.1093/mnras/stae714 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3582M 529, 3582

    2024 doi

  42. [50]

    E., 1991, , https://ui.adsabs.harvard.edu/abs/1991A&A...246..417M 246, 417

    Moenchmeyer R., Schaefer G., Mueller E., Kates R. E., 1991, , https://ui.adsabs.harvard.edu/abs/1991A&A...246..417M 246, 417

    1991

  43. [51]

    Morozova V., Radice D., Burrows A., Vartanyan D., 2018, @doi [ ] 10.3847/1538-4357/aac5f1 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861...10M 861, 10

    2018 doi

  44. [52]

    M \"u ller B., 2016, @doi [ ] 10.1017/pasa.2016.40 , https://ui.adsabs.harvard.edu/abs/2016PASA...33...48M 33, e048

    2016 doi

  45. [53]

    u ller B., Janka H.-T., Marek A., Hanke F., Wongwatha-narat A., M \

    M \"u ller B., Janka H.-T., Marek A., Hanke F., Wongwatha-narat A., M \"u ller E., 2011, in Hamburg Neutrinos from Supernova Explosions. p. 14 ( @eprint arXiv 1112.1913 ), @doi 10.48550/arXiv.1112.1913

    2011 doi arXiv

  46. [54]

    T., Wongwathanarat A., 2012, @doi [ ] 10.1051/0004-6361/201117611 , https://ui.adsabs.harvard.edu/abs/2012A&A...537A..63M 537, A63

    M \"u ller E., Janka H. T., Wongwathanarat A., 2012, @doi [ ] 10.1051/0004-6361/201117611 , https://ui.adsabs.harvard.edu/abs/2012A&A...537A..63M 537, A63

    2012 doi

  47. [55]

    K., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/24/245005 , https://ui.adsabs.harvard.edu/abs/2015CQGra..32x5005N 32, 245005

    Nuttall L. K., et al., 2015, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/32/24/245005 , https://ui.adsabs.harvard.edu/abs/2015CQGra..32x5005N 32, 245005

    2015 doi

  48. [56]

    D., et al., 2013, @doi [ ] 10.1088/0004-637X/768/2/115 , https://ui.adsabs.harvard.edu/abs/2013ApJ...768..115O 768, 115

    Ott C. D., et al., 2013, @doi [ ] 10.1088/0004-637X/768/2/115 , https://ui.adsabs.harvard.edu/abs/2013ApJ...768..115O 768, 115

    2013 doi

  49. [57]

    J., Sathyaprakash B

    Owen B. J., Sathyaprakash B. S., 1999, @doi [ ] 10.1103/PhysRevD.60.022002 , https://ui.adsabs.harvard.edu/abs/1999PhRvD..60b2002O 60, 022002

    1999 doi

  50. [58]

    A., Riera-Guasp M., Lee S

    Pons-Llinares J., Antonino-Daviu J. A., Riera-Guasp M., Lee S. B., Kang T.-j., Yang C., 2014, IEEE Transactions on Industrial Electronics, 62, 1791

    2014

  51. [59]

    Powell J., M \"u ller B., 2019, @doi [ ] 10.1093/mnras/stz1304 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.1178P 487, 1178

    2019 doi

  52. [60]

    Powell J., M \"u ller B., 2020, @doi [ ] 10.1093/mnras/staa1048 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4665P 494, 4665

    2020 doi

  53. [61]

    A., 2024, @doi [ ] 10.1103/PhysRevD.109.063019 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109f3019P 109, 063019

    Powell J., Iess A., Llorens-Monteagudo M., Obergaulinger M., M \"u ller B., Torres-Forn \'e A., Cuoco E., Font J. A., 2024, @doi [ ] 10.1103/PhysRevD.109.063019 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109f3019P 109, 063019

    2024 doi

  54. [62]

    Punturo M., et al., 2010, @doi [Classical and Quantum Gravity] 10.1088/0264-9381/27/19/194002 , https://ui.adsabs.harvard.edu/abs/2010CQGra..27s4002P 27, 194002

    2010 doi

  55. [63]

    Radice D., Morozova V., Burrows A., Vartanyan D., Nagakura H., 2019, @doi [ ] 10.3847/2041-8213/ab191a , https://ui.adsabs.harvard.edu/abs/2019ApJ...876L...9R 876, L9

    2019 doi

  56. [64]

    D., Abdikamalov E., O'Connor E., Sullivan C., 2017, @doi [ ] 10.1103/PhysRevD.95.063019 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95f3019R 95, 063019

    Richers S., Ott C. D., Abdikamalov E., O'Connor E., Sullivan C., 2017, @doi [ ] 10.1103/PhysRevD.95.063019 , https://ui.adsabs.harvard.edu/abs/2017PhRvD..95f3019R 95, 063019

    2017 doi

  57. [65]

    S., Meyer R., 2009, @doi [ ] 10.1103/PhysRevD.80.102004 , https://ui.adsabs.harvard.edu/abs/2009PhRvD..80j2004R 80, 102004

    R \"o ver C., Bizouard M.-A., Christensen N., Dimmelmeier H., Heng I. S., Meyer R., 2009, @doi [ ] 10.1103/PhysRevD.80.102004 , https://ui.adsabs.harvard.edu/abs/2009PhRvD..80j2004R 80, 102004

    2009 doi

  58. [66]

    C., Liebend \"o rfer M., 2008, @doi [ ] 10.1051/0004-6361:20078577 , https://ui.adsabs.harvard.edu/abs/2008A&A...490..231S 490, 231

    Scheidegger S., Fischer T., Whitehouse S. C., Liebend \"o rfer M., 2008, @doi [ ] 10.1051/0004-6361:20078577 , https://ui.adsabs.harvard.edu/abs/2008A&A...490..231S 490, 231

    2008 doi

  59. [67]

    Suvorova S., Powell J., Melatos A., 2019, @doi [ ] 10.1103/PhysRevD.99.123012 , https://ui.adsabs.harvard.edu/abs/2019PhRvD..99l3012S 99, 123012

    2019 doi

  60. [68]

    J., et al., 2021, @doi [ ] 10.1103/PhysRevD.104.102002 , https://ui.adsabs.harvard.edu/abs/2021PhRvD.104j2002S 104, 102002

    Szczepa \'n czyk M. J., et al., 2021, @doi [ ] 10.1103/PhysRevD.104.102002 , https://ui.adsabs.harvard.edu/abs/2021PhRvD.104j2002S 104, 102002

    2021 doi

  61. [69]

    arXiv:2410.16565

    The LIGO Scientific Collaboration et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.16565 , https://ui.adsabs.harvard.edu/abs/2024arXiv241016565T p. arXiv:2410.16565

    2024 doi arXiv

  62. [70]

    Vartanyan D., Burrows A., Wang T., Coleman M. S. B., White C. J., 2023, @doi [Phys. Rev. D] 10.1103/PhysRevD.107.103015 , 107, 103015

    2023 doi

  63. [71]

    Vedre \ n o-Santos F., Riera-Guasp M., Henao H., Pineda-S \'a nchez M., Puche-Panadero R., 2013, IEEE Transactions on Industrial Electronics, 61, 4947

    2013

  64. [72]

    Wang T., Burrows A., 2023, @doi [ ] 10.3847/1538-4357/ace7b2 , https://ui.adsabs.harvard.edu/abs/2023ApJ...954..114W 954, 114

    2023 doi

  65. [73]

    Wang S., Chen X., Cai G., Chen B., Li X., He Z., 2013a, IEEE Transactions on Signal Processing, 62, 69

  66. [74]

    Wang S., Chen X., Li G., Li X., He Z., 2013b, IEEE Transactions on Instrumentation and Measurement, 63, 1372

  67. [75]

    S., 2024, @doi [ ] 10.1103/PhysRevD.109.103004 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109j3004W 109, 103004

    Wu Z., Hu Y.-M., Fan H.-M., Heng I. S., 2024, @doi [ ] 10.1103/PhysRevD.109.103004 , https://ui.adsabs.harvard.edu/abs/2024PhRvD.109j3004W 109, 103004

    2024 doi

  68. [77]

    N., et al., 2017b, @doi [arXiv e-prints] 10.48550/arXiv.1701.07325 , https://ui.adsabs.harvard.edu/abs/2017arXiv170107325Y p

    Yakunin K. N., et al., 2017b, @doi [arXiv e-prints] 10.48550/arXiv.1701.07325 , https://ui.adsabs.harvard.edu/abs/2017arXiv170107325Y p. arXiv:1701.07325

    doi arXiv

  69. [78]

    Yang Y., Peng Z., Meng G., Zhang W., 2011, IEEE Transactions on Industrial Electronics, 59, 1612

    2011

  70. [79]

    Yang Y., Zhang W., Peng Z., Meng G., 2012, IEEE Transactions on Industrial Electronics, 60, 3948

    2012

  71. [80]

    Yu G., 2020, @doi [Journal of Sound and Vibration] 10.33232/001c.117147 , 492, 115813

    2020 doi

  72. [81]

    Yu G., Wang Z., Zhao P., 2018, IEEE Transactions on Industrial Electronics, 66, 5441

    2018

  73. [82]

    Yuan Y., Fan X.-L., L \"u H.-J., Sun Y.-Y., Lin K., 2024, @doi [ ] 10.1093/mnras/stae604 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.3235Y 529, 3235

    2024 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.