Programme
Programme
Venue: Kelvin building, University of Glasgow
ZOOM: https://uofglasgow.zoom.us/j/97944538378?pwd=LzVlQTNIeTZYN3lvMW9sazFGWG5iQT09
Morning Monday 11th: g2net research
09:30 Welcome
09:35 A fast and time-efficient glitch classification method: A deep learning-based visual feature extractor for machine learning algorithms (I. Kirbas, Burdur Mehmet Akif Ersoy University)
10:05 Learning to detect continuous gravitational waves (R. Tenorio, University of the Balearic Islands)
10:35 Dynamic pattern recognition with machine learning (M. Lopez, Utrecht University)
11:05 Discussion
11:15 Break
11:45 TBC (F. Morawski, Polish Academy of Sciences)
12:15 Rapid inference of gravitational wave detections (J. Bayley, University of Glasgow)
12:45 Discussion
Afternoon Monday 11th: Stakeholder engagement
13:00 Lunch (provided)
14:00 Welcome and brief introductions (all)
14:15 g2net Introduction (E. Cuoco, chair of g2net, European Gravitational Observatory and Scuola Normale Superiore, Pisa)
14:30 Doctoral/Innovation training networks (EU team, University of Glasgow)
14:45 Physics-informed AI (E. Bentivegna)
15:00 Challenge overviews: what issue would you like to tackle? (max 10 mins per speaker)
- Craft Prospect (C. Hay)
- Urban Big Data Centre (C. Lido + team)
- Wavefier (A. Iess)
- Wee-g gravimeter (K. Toland & H. Rackoczi)
- ML In The Wild (K. Nordström)
15:15 Divide into small groups for discussion
16:00 Break
16:30 Summary of discussions and ideas to follow-up
17:00 End
Morning Tuesday 12th: g2net research
10:00 Normaling flows for gravitational-wave template banks (S. Schmidt, Utrecht University)
10:30 Importance nested sampling with nessai for gravitational-wave inference (M. Williams, University of Glasgow)
11:00 Physics-informed AI (E. Bentivegna)
11:15 Break
11:45 A Novel Approach to Gravitational Wave Analysis of Compact Binaries & the search for Primordial Black Hole Dark Matter (A. Roy)
12:15 Effect of approximant selection on neural network parameter estimation (O.Freitas)
12:45-14:00 Lunch (on your own)