Harnessing the potential of PyStoch: detecting continuous gravitational waves from interesting supernova remnant targets

Abstract

Detecting continuous gravitational waves (CWs) is challenging due to their weak amplitude and high computational demands, especially with poorly constrained source parameters. Stochastic gravitational-wave background (SGWB) searches using cross-correlation techniques can identify unresolved astrophysical sources, including CWs, at lower computational cost, albeit with reduced sensitivity. This motivates a hybrid approach where SGWB algorithms act as a first-pass filter to identify CW candidates for follow-up with dedicated CW pipelines. We evaluated the discovery potential of the SGWB analysis tool PyStoch for detecting CWs, using simulated signals from spinning down NSs. We then applied the method to data from the third LIGO-Virgo-KAGRA observing run (O3), covering the (20-1726) Hz frequency band, and targeting four supernova remnants: Vela Jr., G347.3-0.5, Cassiopeia A, and the NS associated with the 1987A supernova remnant. If necessary, significant candidates are followed up using the 5-vector Resampling and Band-Sampled Data Frequency-Hough techniques. However, since no interesting candidates were identified in the real O3 analysis, we set 95\% confidence-level upper limits on the CW strain amplitude $h_0$. The most stringent limit was obtained for Cassiopeia A, and is $h_0 = 1.13 \times 10^{-25}$ at $201.57$ Hz with a frequency resolution of $1/32$ Hz. As for the other targets, the best upper limits have been set with the same frequency resolution, and correspond to $h_0 = 1.20 \times 10^{-25} $ at $202.16$ Hz for G347.3-0.5, $1.20 \times 10^{-25}$ at $217.81$ Hz for Vela Jr., and $1.47 \times 10^{-25}$ at $186.41$ Hz for the NS in the 1987A supernova remnant.