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arxiv: 2510.19047 · v2 · pith:4Q3YJJWQnew · submitted 2025-10-21 · 🌀 gr-qc · astro-ph.IM

Global time-frequency search for stellar-mass binary black holes in LISA

Diganta Bandopadhyay , Christian E. A. Chapman-Bird , Alberto Vecchio This is my paper

Pith reviewed 2026-05-25 07:58 UTC · model grok-4.3

classification 🌀 gr-qc astro-ph.IM
keywords LISAgravitational wavesbinary black holessemi-coherent searchtime-frequency analysisdata gapsstellar-mass binariessignal detection
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The pith

A time-frequency adaptive semi-coherent search detects stellar-mass binary black holes in LISA data at coherent signal-to-noise ratios as low as 11-14.

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

The paper introduces a complete pipeline that detects and characterizes gravitational wave signals from the inspiral of stellar-mass binary black holes using LISA observations. It implements an efficient time-frequency version of an adaptive semi-coherent detection statistic shown to handle non-stationary noise and gaps of varying length. Deployed on the two-year Yorsh data challenge, the method recovers signals across the full parameter space for aligned spins and eccentricity at most 0.01, reaching down to coherent signal-to-noise ratios of approximately 11-14. The computation finishes in roughly one day on about 40 GPUs and the same techniques are noted as relevant to extreme-mass-ratio inspirals.

Core claim

We present a complete pipeline for detecting and characterizing gravitational waves produced by the inspiral of stellar-mass binary black holes in LISA data. The analysis framework relies on an efficient time-frequency implementation of an adaptive semi-coherent detection statistic, which we show to be robust against non-stationary noise and the presence of gaps of varying duration and cadence. The search is able to detect signals with coherent signal-to-noise ratios as low as approximately 11-14 over the full parameter space of binary black holes with spins aligned to the orbital angular momentum and orbital eccentricity at most 0.01 when deployed on the 2-year-long LISA Data Challenge Yors

What carries the argument

An efficient time-frequency implementation of an adaptive semi-coherent detection statistic that scans the data while remaining stable under noise variations and interruptions.

If this is right

  • The pipeline recovers signals over the full parameter space for aligned-spin, low-eccentricity binary black holes.
  • Performance holds in the presence of non-stationary noise and gaps of varying duration and cadence.
  • The entire search completes within one day on approximately 40 GPUs.
  • The same techniques extend directly to searches for extreme-mass-ratio inspirals in LISA data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could serve as a template for efficient global searches across multiple LISA source classes that share similar signal morphologies.
  • If the statistic proves stable under relaxed assumptions on spin and eccentricity, the same pipeline structure could cover a wider fraction of the binary black hole parameter space without major redesign.
  • Combining this time-frequency approach with existing matched-filter pipelines might reduce the overall computational load for LISA data analysis by handling the initial detection stage.

Load-bearing premise

The adaptive semi-coherent detection statistic remains effective when restricted to aligned spins and eccentricity at most 0.01 amid the specific non-stationary noise and gap patterns of the Yorsh data challenge.

What would settle it

Inject known signals with coherent signal-to-noise ratios of 11-14 into the Yorsh data challenge and verify whether the pipeline recovers them at the claimed rate.

Figures

Figures reproduced from arXiv: 2510.19047 by Alberto Vecchio, Christian E. A. Chapman-Bird, Diganta Bandopadhyay.

Figure 1
Figure 1. Figure 1: FIG. 1. Time-frequency representations of TDI channel [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. The results of the search on both 100% and 85% duty cycle datasets. Top-left: Maximum detection statistic [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Wall-time per set of source parameters in the eval [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Computational wall-time per evaluation of Eq. (8) in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Relative error in the recovery of [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison of marginal posterior distributions for source 6 (not detected by our pipeline) with successfully identified [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
read the original abstract

We present a complete pipeline for detecting and characterizing gravitational waves (GWs) produced by the inspiral of stellar-mass binary black holes in data from the Laser Interferometer Space Antenna (LISA). The analysis framework relies on an efficient time-frequency implementation of an adaptive semi-coherent detection statistic, which we show to be robust against non-stationary noise and the presence of gaps of varying duration and cadence. The search is able to detect signals with coherent signal-to-noise ratios as low as $\approx 11-14$ over the full parameter space of binary black holes with spins aligned to the orbital angular momentum and orbital eccentricity $\le 0.01$ when deployed on the 2-year-long LISA Data Challenge Yorsh. The search can be run within a day using $\approx 40$ GPUs. The techniques presented here have wider applications in GW astronomy, in particular the search for extreme-mass-ratio inspirals in LISA data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a complete pipeline for detecting and characterizing gravitational waves from the inspiral of stellar-mass binary black holes in LISA data. The framework uses an efficient time-frequency implementation of an adaptive semi-coherent detection statistic, claimed to be robust against non-stationary noise and gaps of varying duration and cadence. It reports successful detection of signals with coherent SNR as low as ≈11-14 over the full parameter space of aligned-spin, eccentricity ≤0.01 binaries when applied to the 2-year Yorsh LISA Data Challenge, with the search completing in a day on ≈40 GPUs, and notes wider applicability to searches for extreme-mass-ratio inspirals.

Significance. If the performance and robustness claims hold under detailed scrutiny, the work would be significant for LISA data analysis by providing a practical, computationally efficient global search method capable of reaching low coherent SNR thresholds for a key source population. Deployment and testing on a public data challenge supports reproducibility and enables direct method comparisons. The extension to other LISA signals such as EMRIs broadens the potential impact.

major comments (2)
  1. [Abstract] Abstract: the headline claim of detection at coherent SNR ≈11-14 is presented without derivation details, error bars, baseline comparisons to other methods, or quantitative validation metrics on injected signals beyond the headline assertion, leaving the central performance claim only moderately supported.
  2. [Abstract] Abstract (robustness claim): the assertion that the adaptive semi-coherent statistic remains effective under the exact gap cadence, duration distribution, and non-stationary noise properties of the Yorsh challenge is load-bearing for the uniform low-SNR reach across the aligned-spin, e≤0.01 space, yet no region-specific tests or degradation metrics are referenced to confirm this holds uniformly.
minor comments (1)
  1. The manuscript would benefit from explicit definitions and notation for the time-frequency implementation of the adaptive statistic and the coherent SNR measure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful review and constructive feedback. We address the two major comments on the abstract below. Where appropriate, we have revised the abstract to improve clarity and cross-referencing while preserving its concise nature.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of detection at coherent SNR ≈11-14 is presented without derivation details, error bars, baseline comparisons to other methods, or quantitative validation metrics on injected signals beyond the headline assertion, leaving the central performance claim only moderately supported.

    Authors: The abstract summarizes the key result; the derivation of the SNR threshold, including the injection campaign, detection statistics, and recovery metrics, is provided in Sections 4 and 5, with quantitative results in Figures 3–6 and Table 1. We have added a brief clause to the abstract directing readers to these sections for the supporting analysis. Error bars are not applicable because the reported range reflects the lowest coherent SNR at which every injected signal in the tested parameter space was recovered. Direct baseline comparisons to other pipelines are outside the scope of this work, which presents and validates our specific time-frequency semi-coherent approach on the Yorsh challenge. revision: partial

  2. Referee: [Abstract] Abstract (robustness claim): the assertion that the adaptive semi-coherent statistic remains effective under the exact gap cadence, duration distribution, and non-stationary noise properties of the Yorsh challenge is load-bearing for the uniform low-SNR reach across the aligned-spin, e≤0.01 space, yet no region-specific tests or degradation metrics are referenced to confirm this holds uniformly.

    Authors: The robustness claim is supported by the pipeline’s successful recovery of all injected signals across the full aligned-spin, e≤0.01 parameter space when run on the actual Yorsh data set, which incorporates the challenge’s specific gap structure and non-stationary noise. Section 3 details the adaptive statistic’s design for handling gaps and non-stationarity. We have added a cross-reference in the abstract to Section 3 and the relevant figures. While region-by-region degradation metrics are not separately tabulated, the uniform low-SNR performance across the entire space in the presence of the challenge’s exact noise and gap properties provides the primary validation. revision: partial

Circularity Check

0 steps flagged

No significant circularity; performance validated on external public data challenge

full rationale

The paper presents a pipeline using an adaptive semi-coherent statistic and demonstrates its detection reach (coherent SNR ≈11-14) on the independent LISA Data Challenge Yorsh dataset. The abstract explicitly ties the performance claim to deployment on this external 2-year challenge data with its specific noise and gap properties. No equations, self-citations, or steps in the provided text reduce the central result to a fit, definition, or prior author work by construction. The result is externally falsifiable on public challenge data rather than tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; none can be identified from the given text.

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discussion (0)

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Reference graph

Works this paper leans on

75 extracted references · 75 canonical work pages · 33 internal anchors

  1. [1]

    A. G. Abacet al.(LIGO Scientific, VIRGO, KA- GRA), arXiv e-prints , arXiv:2508.18081 (2025), arXiv:2508.18081 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  2. [2]

    Advanced LIGO

    J. Aasiet al.(LIGO Scientific), Class. Quant. Grav.32, 074001 (2015), arXiv:1411.4547 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  3. [3]

    Advanced Virgo: a 2nd generation interferometric gravitational wave detector

    F. Acerneseet al.(VIRGO), Class. Quant. Grav.32, 024001 (2015), arXiv:1408.3978 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  4. [4]

    Akutsuet al.(KAGRA), PTEP2021, 05A101 (2021), arXiv:2005.05574 [physics.ins-det]

    T. Akutsuet al.(KAGRA), PTEP2021, 05A101 (2021), arXiv:2005.05574 [physics.ins-det]

    work page arXiv 2021

  5. [5]

    Punturoet al., Classical and Quantum Gravity27, 194002 (2010)

    M. Punturoet al., Classical and Quantum Gravity27, 194002 (2010)

    work page 2010

  6. [6]

    A Horizon Study for Cosmic Explorer: Science, Observatories, and Community

    M. Evanset al., arXiv e-prints , arXiv:2109.09882 (2021), arXiv:2109.09882 [astro-ph.IM]

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  7. [7]

    T. L. Collaboration, Monthly Notices of the Royal Astronomical Society524, 5984 (2023), https://academic.oup.com/mnras/article- pdf/524/4/5984/52633671/stad588.pdf

    work page 2023

  8. [8]

    A. G. Abacet al., ApJ973, 132 (2024), arXiv:2308.03822 [astro-ph.HE]

    work page arXiv 2024

  9. [9]

    Searching for Gravitational Waves from Compact Binaries with Precessing Spins

    I. Harry, S. Privitera, A. Boh´ e, and A. Buonanno, PRD 94, 024012 (2016), arXiv:1603.02444 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  10. [10]

    Laser Interferometer Space Antenna

    P. Amaro-Seoaneet al., arXiv e-prints , arXiv:1702.00786 (2017), arXiv:1702.00786 [astro-ph.IM]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  11. [11]

    LISA Definition Study Report

    M. Colpiet al., arXiv e-prints , arXiv:2402.07571 (2024), arXiv:2402.07571 [astro-ph.CO]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  12. [12]

    The promise of multi-band gravitational wave astronomy

    A. Sesana, PRL116, 231102 (2016), arXiv:1602.06951 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  13. [13]

    Multiband gravitational-wave event rates and stellar physics

    D. Gerosa, S. Ma, K. W. K. Wong, E. Berti, R. O’Shaughnessy, Y. Chen, and K. Belczynski, PRD 99, 103004 (2019), arXiv:1902.00021 [astro-ph.HE]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  14. [14]

    C. J. Moore, D. Gerosa, and A. Klein, MNRAS488, L94 (2019), arXiv:1905.11998 [astro-ph.HE]

    work page arXiv 2019

  15. [15]

    Klein, G

    A. Klein, G. Pratten, R. Buscicchio, P. Schmidt, C. J. Moore, E. Finch, A. Bonino, L. M. Thomas, N. Williams, D. Gerosa, S. McGee, M. Nicholl, and A. Vecchio, arXiv e- prints , arXiv:2204.03423 (2022), arXiv:2204.03423 [astro- ph.HE]

    work page arXiv 2022

  16. [16]

    P. A. Seoaneet al.(LISA), Living Rev. Rel.26, 2 (2023), arXiv:2203.06016 [gr-qc]

    work page internal anchor Pith review arXiv 2023

  17. [17]

    K. G. Arunet al.(LISA), Living Rev. Rel.25, 4 (2022), arXiv:2205.01597 [gr-qc]

    work page arXiv 2022

  18. [18]

    J. R. Gair, L. Barack, T. Creighton, C. Cutler, S. L. Larson, E. S. Phinney, and M. Vallisneri, Class. Quant. Grav.21, S1595 (2004), arXiv:gr-qc/0405137

    work page internal anchor Pith review Pith/arXiv arXiv 2004

  19. [19]

    A. J. K. Chua and C. J. Cutler, Phys. Rev. D106, 124046 (2022), arXiv:2109.14254 [gr-qc]

    work page arXiv 2022

  20. [20]

    S. H. Strub, L. Speri, and D. Giardini, arXiv e-prints (2025), arXiv:2505.17814 [gr-qc]

    work page arXiv 2025

  21. [21]

    J. R. Gair and G. Jones, Class. Quant. Grav.24, 1145 (2007), arXiv:gr-qc/0610046

    work page internal anchor Pith review Pith/arXiv arXiv 2007

  22. [22]

    Detecting extreme mass ratio inspirals with LISA using time-frequency methods

    L.-q. Wen and J. R. Gair, Class. Quant. Grav.22, S445 (2005), arXiv:gr-qc/0502100

    work page internal anchor Pith review Pith/arXiv arXiv 2005

  23. [23]

    Zhang, C

    X.-T. Zhang, C. Messenger, N. Korsakova, M. L. Chan, Y.-M. Hu, and J.-d. Zhang, Phys. Rev. D105, 123027 (2022), arXiv:2202.07158 [astro-ph.HE]

    work page arXiv 2022

  24. [24]

    P. S. Cole, J. Alvey, L. Speri, C. Weniger, U. Bhard- waj, D. Gerosa, and G. Bertone, arXiv e-prints (2025), arXiv:2505.16795 [gr-qc]

    work page arXiv 2025

  25. [25]

    Ye, H.-M

    C.-Q. Ye, H.-M. Fan, A. Torres-Orjuela, J.-d. Zhang, and Y.-M. Hu, Phys. Rev. D109, 124034 (2024), arXiv:2310.03520 [gr-qc]

    work page arXiv 2024

  26. [26]

    Y. Wang, Y. Shang, S. Babak, Y. Shang, and S. Babak, Phys. Rev. D86, 104050 (2012), arXiv:1207.4956 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  27. [27]

    Buscicchio, A

    R. Buscicchio, A. Klein, E. Roebber, C. J. Moore, D. Gerosa, E. Finch, and A. Vecchio, PRD104, 044065 (2021), arXiv:2106.05259 [astro-ph.HE]

    work page arXiv 2021

  28. [28]

    M. C. Digman and N. J. Cornish, PRD108, 023022 (2023), arXiv:2212.04600 [gr-qc]

    work page arXiv 2023

  29. [29]

    Bandopadhyay and C

    D. Bandopadhyay and C. J. Moore, Phys. Rev. D108, 084014 (2023), arXiv:2305.18048 [gr-qc]

    work page arXiv 2023

  30. [30]

    Bandopadhyay and C

    D. Bandopadhyay and C. J. Moore, Phys. Rev. D110, 103026 (2024)

    work page 2024

  31. [31]

    Bandopadhyay and C

    D. Bandopadhyay and C. J. Moore, Phys. Rev. D111, 044039 (2025)

    work page 2025

  32. [32]

    H. Wang, M. J. Williams, I. Harry, and Y.-M. Hu, arXiv e- prints , arXiv:2510.07174 (2025), arXiv:2510.07174 [astro- ph.HE]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  33. [33]

    H. Wang, I. Harry, A. Nitz, and Y.-M. Hu, PRD109, 063029 (2024), arXiv:2304.10340 [astro-ph.HE]

    work page arXiv 2024

  34. [34]

    S. Wu, A. H. Nitz, I. Harry, S. Babak, M. J. Williams, C. Capano, and C. Weaving, arXiv e-prints (2025), arXiv:2506.01898 [gr-qc]

    work page arXiv 2025

  35. [35]

    Toubiana, S

    A. Toubiana, S. Marsat, E. Barausse, S. Babak, and J. Baker, Phys. Rev. D101, 104038 (2020), arXiv:2004.03626 [gr-qc]

    work page arXiv 2020

  36. [36]

    Gupta, S

    A. Gupta, S. Datta, S. Kastha, S. Borhanian, K. G. Arun, and B. S. Sathyaprakash, Phys. Rev. Lett.125, 201101 (2020), arXiv:2005.09607 [gr-qc]

    work page arXiv 2020

  37. [37]

    B. P. Abbottet al., PRL116, 061102 (2016), arXiv:1602.03837 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  38. [38]

    B. P. Abbottet al., Physical Review X6, 041015 (2016), arXiv:1606.04856 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  39. [39]

    B. P. Abbottet al., Physical Review X9, 031040 (2019), arXiv:1811.12907 [astro-ph.HE]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  40. [40]

    Abbottet al., PRL125, 101102 (2020), arXiv:2009.01075 [gr-qc]

    R. Abbottet al., PRL125, 101102 (2020), arXiv:2009.01075 [gr-qc]

    work page arXiv 2020

  41. [41]

    GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

    R. Abbottet al., Physical Review X11, 021053 (2021), arXiv:2010.14527 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  42. [42]

    GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run

    R. Abbottet al., Physical Review X13, 041039 (2023), arXiv:2111.03606 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  43. [43]

    A. G. Abacet al., arXiv e-prints , arXiv:2507.08219 (2025), arXiv:2507.08219 [astro-ph.HE]. 7

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  44. [44]

    A. G. Abacet al.(LIGO Scientific, VIRGO, KA- GRA), arXiv e-prints , arXiv:2508.18082 (2025), arXiv:2508.18082 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  45. [45]

    A. G. Abacet al.(KAGRA, Virgo, LIGO Scientific), Phys. Rev. Lett.135, 111403 (2025), arXiv:2509.08054 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  46. [46]

    Buscicchio, J

    R. Buscicchio, J. Torrado, C. Caprini, G. Nardini, N. Kar- nesis, M. Pieroni, and A. Sesana, J. Cosmology Astropart. Phys.2025, 084 (2025), arXiv:2410.18171 [astro-ph.HE]

    work page arXiv 2025

  47. [47]

    S. E. de Mink and A. King, ApJ839, L7 (2017), arXiv:1703.07794 [astro-ph.HE]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  48. [48]

    Yi and K

    S.-X. Yi and K. S. Cheng, ApJ884, L12 (2019), arXiv:1909.08384 [astro-ph.HE]

    work page arXiv 2019

  49. [49]

    Tenorio and D

    R. Tenorio and D. Gerosa, arXiv e-prints , arXiv:2502.11823 (2025), arXiv:2502.11823 [gr-qc]

    work page arXiv 2025

  50. [50]

    T. A. Prince, M. Tinto, S. L. Larson, and J. W. Armstrong, Phys. Rev. D66, 122002 (2002), arXiv:gr-qc/0209039

    work page internal anchor Pith review Pith/arXiv arXiv 2002

  51. [51]

    A. J. K. Chua, C. J. Moore, and J. R. Gair, Phys. Rev. D96, 044005 (2017), arXiv:1705.04259 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  52. [52]

    N. J. Cornish and R. W. Hellings, Classical and Quantum Gravity20, 4851 (2003), arXiv:gr-qc/0306096 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2003

  53. [53]

    Tinto and S

    M. Tinto and S. V. Dhurandhar, Living Reviews in Rela- tivity24, 1 (2021), arXiv:gr-qc/0409034 [gr-qc]

    work page arXiv 2021

  54. [54]

    Valencia and S

    J. Valencia and S. Husa, PRD112, 044020 (2025), arXiv:2505.09600 [gr-qc]

    work page arXiv 2025

  55. [55]

    Servignat, Yorsh stellar black hole binaries dataset (2025), lDC Technical note

    G. Servignat, Yorsh stellar black hole binaries dataset (2025), lDC Technical note

    work page 2025

  56. [56]

    Towards models of gravitational waveforms from generic binaries: A simple approximate mapping between precessing and non-precessing inspiral signals

    P. Schmidt, M. Hannam, and S. Husa, Phys. Rev. D86, 104063 (2012), arXiv:1207.3088 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  57. [57]

    S. Khan, S. Husa, M. Hannam, F. Ohme, M. P¨ urrer, X. J. Forteza, and A. Boh´ e, PRD93, 044007 (2016), arXiv:1508.07253 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  58. [58]

    Gravitational-wave phasing for low-eccentricity inspiralling compact binaries to 3PN order

    B. Moore, M. Favata, K. G. Arun, and C. K. Mishra, Phys. Rev. D93, 124061 (2016), arXiv:1605.00304 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  59. [59]

    Favata, C

    M. Favata, C. Kim, K. G. Arun, J. Kim, and H. W. Lee, PRD105, 023003 (2022), arXiv:2108.05861 [gr-qc]

    work page arXiv 2022

  60. [60]

    L. D. C. working group, Lisa data challenge software (2022)

    work page 2022

  61. [61]

    Babak, M

    S. Babak, M. L. Jeune, A. Petiteau, and M. Vallisneri, Lisa data challenge: Sangria (2020), lDC technical note

    work page 2020

  62. [62]

    Baghi, N

    Q. Baghi, N. Korsakova, J. Slutsky, E. Castelli, N. Kar- nesis, and J.-B. Bayle, Phys. Rev. D105, 042002 (2022), arXiv:2112.07490 [gr-qc]

    work page arXiv 2022

  63. [63]

    M. C. Digman and N. J. Cornish, ApJ940, 10 (2022), arXiv:2206.14813 [astro-ph.IM]

    work page arXiv 2022

  64. [64]

    Burke, M

    O. Burke, M. Muratore, and G. Woan, arXiv e-prints , arXiv:2510.06406 (2025), arXiv:2510.06406 [gr-qc]

    work page arXiv 2025

  65. [65]

    Burke, S

    O. Burke, S. Marsat, J. R. Gair, and M. L. Katz, PRD 111, 124053 (2025), arXiv:2502.17426 [gr-qc]

    work page arXiv 2025

  66. [66]

    N. J. Cornish and L. J. Rubbo, Phys. Rev. D67, 022001 (2003), [Erratum: Phys.Rev.D 67, 029905 (2003)], arXiv:gr-qc/0209011

    work page internal anchor Pith review Pith/arXiv arXiv 2003

  67. [67]

    Martens and E

    W. Martens and E. Joffre, Journal of the Astronautical Sciences68, 402 (2021), arXiv:2101.03040 [gr-qc]

    work page arXiv 2021

  68. [68]

    M. L. Katz, J.-B. Bayle, A. J. K. Chua, and M. Vallisneri, Phys. Rev. D106, 103001 (2022)

    work page 2022

  69. [69]

    B. J. Owen, Phys. Rev. D53, 6749 (1996), arXiv:gr- qc/9511032

    work page arXiv 1996

  70. [70]

    Sakonet al., Phys

    S. Sakonet al., Phys. Rev. D109, 044066 (2024), arXiv:2211.16674 [gr-qc]

    work page arXiv 2024

  71. [71]

    Chapman-Bird and D

    C. Chapman-Bird and D. Bandopadhyay, dig07/yorsh- end-to-end-search: Initial paper release (2025)

    work page 2025

  72. [72]

    Abramowitz and I

    M. Abramowitz and I. A. Stegun,Handbook of Mathemat- ical Functions with Formulas, Graphs, and Mathematical Tables(Dover Publications Inc., New York, 1964) p. 300

    work page 1964

  73. [73]

    DLMF,NIST Digital Library of Mathematical Functions, https://dlmf.nist.gov/, Release 1.2.4 of 2025-03-15, equations 7.5.3 and 7.5.4; F. W. J. Olver, A. B. Olde Daalhuis, D. W. Lozier, B. I. Schneider, R. F. Boisvert, C. W. Clark, B. R. Miller, B. V. Saunders, H. S. Cohl, and M. A. McClain, eds

    work page 2025

  74. [74]

    Fourier-domain modulations and delays of gravitational-wave signals

    S. Marsat and J. G. Baker, arXiv e-prints , arXiv:1806.10734 (2018), arXiv:1806.10734 [gr-qc]

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  75. [75]

    I. W. Harry, B. Allen, and B. S. Sathyaprakash, PRD80, 104014 (2009), arXiv:0908.2090 [gr-qc]. Supplemental Material Time-frequency search statistic —Here we de- tail our procedure for constructing the detection statis- tic, Eq. (1), in the time-frequency domain by evaluating Eq. (3) instead of the more computationally expensive Eq. (2), and its massively...

    work page internal anchor Pith review Pith/arXiv arXiv 2009