A new analysis of gravitational wave (and other data) from GW170817 on 2017-Aug-17 has been published, strongly suggesting that the merger of two neutron stars resulted in a large, rapidly rotating neutron star, rather than a black hole. From the abstract of the recent open access Letter in MNRAS Observational evidence for extended emission to GW170817:

[...]Here, we report on a possible detection of extended emission (EE) in gravitational radiation during GRB170817A: a descending chirp with characteristic time-scale τs = 3.01 ± 0.2 s in a (H1,L1)-spectrogram up to 700 Hz with Gaussian equivalent level of confidence greater than 3.3σ based on causality alone following edge detection applied to (H1,L1)-spectrograms merged by frequency coincidences. Additional confidence derives from the strength of this EE. The observed frequencies below 1 kHz indicate a hypermassive magnetar rather than a black hole, spinning down by magnetic winds and interactions with dynamical mass ejecta.

Maurice H P M van Putten and Massimo Della Valle, Monthly Notices of the Royal Astronomical Society: Letters, Volume 482, Issue 1, 1 January 2019, Pages L46–L49, https://doi.org/10.1093/mnrasl/sly166

Discussion in paper points to supplementary data and in the introduction to that document, it says:

For GW170817A/GRB170817A, we perform a model-independent deep search for broadband extended gravitational-wave emission in 2048 s (LIGO 2017) of data at 4096 Hz according to Fig. A1 comprising

  • Pre-processing: cleaning and glitch removal (Abbott et al. 2017a) followed by whitening of H1, L1 and V1 data;
  • Singe detector spectrograms by GPU-accelerated butterfly filtering of H1, L1 and V1 by matched filtering over a dense bank of time-symmetric chirp-like templates (van Putten et al. 2014; van Putten 2017);
  • Merging spectrograms by coincidences in frequency or amplitude, producing merged spectrograms as input to image analysis (van Putten 2018).

I am having trouble understanding what GPU-accelerated butterfly filtering of H1, L1 and V1 by matched filtering over a dense bank of time-symmetric chirp-like templates means. van Putten et al. 2014 in the supplementary data document refers to both BROADBAND TURBULENT SPECTRA IN GAMMA-RAY BURST LIGHT CURVES and to as well. These appear to be thorough explanations, but pretty in-depth.

Question: Is it possible to explain the basics of what "GPU-accelerated butterfly filtering of H1, L1 and V1 by matched filtering over a dense bank of time-symmetric chirp-like templates" means? A dense bank of chirp-like templates sounds like it could be analogous to a wavelet-type analysis, but with basis waveforms tailored to this specific problem.

First image is a cropped and annotated version of the second which ia from here.

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Figure2. Ascending–descending chirp in the (H1,L1)-spectrogram produced by the double neutron star merger GW170817 concurrent with GRB170817A (Goldstein et al. 2017) past coalescence (tc = 1842.43 s). Minor accompanying features around 100 Hz ( 1840-1852 s) are conceivably due to dynamical mass ejecta. Colour coding (blue-to-yellow) is proportional to amplitude defined by butterfly output ρ of time-symmetric chirp-like template correlations to data.

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    $\begingroup$ GPU-accelerated butterfly filtering is a computer-program filtering algorithm, to filter only signals out of the data. time-symmetric chirp-like templates are templates of signals of various types (both GW and non GW). The detection algorithm of the GW is that, the system has a library (i.e., a dense bank) of various known types of signals, and how they would look like. From that, once you have data you can compute detection probability like false-alarm one. $\endgroup$ Nov 16, 2018 at 13:48
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    $\begingroup$ @KornpobBhirombhakdi thanks! That's an excellent functional summary and I appreciate it. I am hoping that when an answer is posted, it will explain a bit more of the mathematical aspects. For example, I certainly understand that "chirp-like templates are templates of signals of various types" but that's a bit circular. I'm also comfortable with using a library search (rather than an optimization using a basis set) and so that is very helpful. I wonder what one template might "look like". Would it be simply a 1 millisecond long strain waveform? $\endgroup$
    – uhoh
    Nov 16, 2018 at 15:03
  • $\begingroup$ @KornpobBhirombhakdi if you know of (or can find) a reference that describes how the library is built, that might help towards finding an answer. I can read it and then post an answer myself if nobody else is interested. Thanks! $\endgroup$
    – uhoh
    Dec 5, 2018 at 8:41
  • $\begingroup$ have you checked this paper, Abbott B. P. et al., 2017a, Phys. Rev. Lett., 119, 161101? In section 2. Data, there are references about the data processing. And, section 3 desbribes stuff leading to the false alarm rate at the end. $\endgroup$ Dec 5, 2018 at 16:25
  • $\begingroup$ @KornpobBhirombhakdi I did talk about the results of Abbot 2017 here but I will go back now and have a look in section 2 (and 3) and references therein, thank you! $\endgroup$
    – uhoh
    Dec 5, 2018 at 16:53


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