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Abstract The nanohertz frequency band explored by pulsar timing arrays provides a unique discovery space for gravitational wave (GW) signals. In addition to signals from anticipated sources, such as those from supermassive black hole binaries, some previously unimagined sources may emit transient GWs (a.k.a. bursts) with unknown morphology. Unmodeled transients are not currently searched for in this frequency band, and they require different techniques from those currently employed. Possible sources of such GW bursts in the nanohertz regime are parabolic encounters of supermassive black holes, cosmic string cusps and kinks, or other, as-yet-unknown phenomena. In this paper we present BayesHopperBurst , a Bayesian search algorithm capable of identifying generic GW bursts by modeling both coherent and incoherent transients as a sum of Morlet–Gabor wavelets. A trans-dimensional reversible jump Markov chain Monte Carlo sampler is used to select the number of wavelets best describing the data. We test BayesHopperBurst on various simulated datasets including different combinations of signals and noise transients. Its capability to run on real data is demonstrated by analyzing data of the pulsar B1855 + 09 from the NANOGrav 9 year dataset. Based on a simulated dataset resembling the NANOGrav 12.5 year data release, we predict that at our most sensitive time–frequency location we will be able to probe GW bursts with a root-sum-squared amplitude higher than ∼5 × 10 −11 Hz −1/2 , which corresponds to ∼40 M ⊙ c 2 emitted in GWs at a fiducial distance of 100 Mpc.
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The Scattering Transform Network with Generalized Morse Wavelets and its Application to Music Genre Classification
We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal classification problems. The GMWs form a parameterized family of truly analytic wavelets while the Morlet wavelets are only approximately analytic. The analyticity of underlying wavelet filters in the STN is particularly important for nonstationary oscillatory signals such as music signals because it improves interpretability of the STN representations by providing multi-scale amplitude and phase (and consequently frequency) information of input signals. We demonstrate the superiority of the GMW-STN over the conventional STN in music genre classification using the so-called GTZAN database. Moreover, we show the performance improvement of the GMW-STN by increasing its number of layers to three over the typical two-layer STN.
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- PAR ID:
- 10415339
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- Proceedings of 2022 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
- Page Range / eLocation ID:
- 25 to 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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