Harnessing the potential of PyStoch: detecting continuous gravitational waves from interesting supernova remnant targets
Claudio Salvadore, Iuri La Rosa, Paola Leaci, Francesco Amicucci, Pia Astone, Sabrina D'Antonio, Luca D'Onofrio, Cristiano Palomba, Lorenzo Pierini, Francesco Safai Tehrani

TL;DR
This paper evaluates PyStoch, a stochastic gravitational-wave background analysis tool, for detecting continuous gravitational waves from supernova remnants, applying it to LIGO-Virgo-KAGRA data and setting upper limits on wave strain.
Contribution
The study demonstrates the application of PyStoch as a first-pass filter for continuous gravitational wave detection in real gravitational-wave data, establishing new upper limits on wave strain for specific supernova remnants.
Findings
No candidates found in O3 data, setting upper limits on wave strain.
Most stringent upper limit for Cassiopeia A at 201.57 Hz: 1.13e-25.
Upper limits for other targets range from 1.20e-25 to 1.47e-25.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
