| File(s) | VIR-0629A-21.pdf (1.1 MB) |
|---|---|
| Document type | Publication |
| Abstract | Inference from gravitational-wave observations relies on the availability of accurate theoretical waveform models to compare with the data. This paper considers the rapid generation of surrogate time-domain waveforms consistent with the gravitational-wave signature of the merger of spin-aligned binary black holes. Building on previous works, a machine-learning model is proposed that allows for highly-accurate waveform regression from a set of examples. An improvement of about an order of magnitude in accuracy with respect to the state of the art is demonstrated, along with a significant speed up in computing time with respect to the reference generation software tools. |
| Author(s) | Cyril Cano, Éric Chassande-Mottin, Nicolas Le Bihan |
| Code | VIR-0629A-21 |
| Code issue time/date | 19:49, Wednesday the 2nd of June, 2021 |
| Referral URL | https://tds.virgo-gw.eu/ql/?c=16877 COPIED |
| Series | Data Analysis Compact Binary Coalescence (CBC) |
| Annex files |