About
Gravitational-wave (GW) astronomy promises groundbreaking discoveries in the coming decades, but its progress is bottlenecked by the computational challenges of large-scale and real-time data analysis. I will present DINGO, a machine learning approach for fast and accurate GW inference that addresses these challenges. DINGO trains generative neural networks to directly estimate probability distributions over GW source parameters. I will explain the core ideas behind DINGO and highlight several machine learning techniques that we developed to adapt modern simulation-based inference to the challenging field of GW data analysis.
00:00:00 Slide 1
00:00:29 Slide 2
00:00:44 Slide 3
00:01:03 Slide 4
00:01:29 Slide 5
00:03:07 Slide 6
00:04:59 Slide 7
00:06:06 Slide 8
00:07:31 Slide 9
00:07:57 Slide 10
00:09:09 Slide 11
The talk
Credits and licence
- Institution
- CERN
- Speaker
- Maximilian Dax
- Year
- 2026
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed