About
Global SMEFT analyses combine a vast range of LHC measurements to construct likelihoods to put constraints on physics beyond the Standard Model. However, constructing and evaluating profile likelihoods for such analyses is computationally intensive and prone to instability and noise. We show how modern numerical techniques, similar to neural importance sampling, can dramatically enhance both efficiency and stability. Specifically, we focus on datasets used in previous SFitter analyses, combining data from the Top sector with Higgs, Di-Boson, and electroweak precision measurements to simultaneously constrain up to 42 Wilson coefficients..
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The talk
Credits and licence
- Institution
- CERN
- Department
- Workshops
- Speaker
- Nikita Schmal
- Year
- 2025
- Language
- English
- Subjects
- Physics
- 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