Speaker
Guillermo Hijano Mendizabal
(University of Zurich (CH))
Description
This talk will summarise a method based on machine learning to play the devil's advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis. We explore this idea in arXiv:2303.15956 by considering the hypothetical possibility that the efficiency to reconstruct a signal is mismodelled in the simulation. Extensions of this method to include hypothetical backgrounds are also discussed, which have the potential to significantly streamline the analysis procedure in a complex experiment.
| Alternate track | 13. Detectors for Future Facilities, R&D, Novel Techniques |
|---|---|
| I read the instructions above | Yes |
Authors
Alexander Mclean Marshall
Andrea Mauri
(Imperial College (GB))
Davide Lancierini
(University of Cambridge (GB))
Guillermo Hijano Mendizabal
(University of Zurich (CH))
Hanae Tilquin
(Imperial College (GB))
Konstantinos Petridis
(University of Bristol (GB))
Mitesh Patel
(Imperial College (GB))
Nicola Serra
(University of Zurich (CH))
Patrick Owen
(University of Zurich (CH))
Shah Rukh Qasim
(University of Zurich (CH))
William Sutcliffe
(University of Zurich (CH))
