Course Syllabus
Titolo
Precision measurements and and search for BSM physics at the LHC
Docente(i)
Ilaria Brivio (Bologna University) and Davide Valsecchi (ETH Zurich)
Lingua
English
Breve descrizione
The experimental aspect: Machine Learning ingredients for precision Physics
Parametrized classifiers for EFT:
- Introduzione a ML e deep learning
- EFT weights morphing
- Learning the likelihood ratio with a parametrized classifier for EFT
- How to choose the best classifier
Transformer models for event interpretation
- The transformer architecture
- Using the transformer to analyze reconstructed particles and regress parton-level particles
- How to use the correct losses for multi-target optimization and obtain the best multi-particle regression
The theoretical aspect: EFT simulation and fits
Advanced topics in SMEFT interpretation at LO
- Quick SMEFT dim 6 recap
- Constraints from EWPOs at LEP and the role of mW
- State-of-the-art global fits and the role played by each ingredient: operator basis, predictions order, measurements, uncertainties, statistical methods.
Even more precision: higher-order SMEFT, in loops and in operator dimension
- SMEFT at dim-6: RGE evolution, operator mixing and their impact in global fits
- SMEFT at dim-8: motivation, technical challenges and case studies
CFU / Ore
2 ECTS
Periodo di erogazione
The course will be given in a blended form, with lectures given in presence and with remote connection at the same time.
Each chapter of the course will feature front lectures and exercises.
The experimental aspects will be discussed on Oct. 10 and 11, while the theory part will be disussed on Oct. 28 and 29.
The detailed timetable will be published soon.
Sustainable Development Goals
Title
Precision measurements and and search for BSM physics at the LHC
Teacher(s)
Ilaria Brivio (Bologna University) and Davide Valsecchi (ETH Zurich)
Language
English
Short description
The experimental aspect: Machine Learning ingredients for precision Physics
Parametrized classifiers for EFT:
Introduzione a ML e deep learning
EFT weights morphing
Learning the likelihood ratio with a parametrized classifier for EFT
How to choose the best classifier
Transformer models for event interpretation
The transformer architecture
Using the transformer to analyze reconstructed particles and regress parton-level particles
How to use the correct losses for multi-target optimization and obtain the best multi-particle regression
The theoretical aspect: EFT simulation and fits
Advanced topics in SMEFT interpretation at LO
Quick SMEFT dim 6 recap
Constraints from EWPOs at LEP and the role of mW
State-of-the-art global fits and the role played by each ingredient: operator basis, predictions order, measurements, uncertainties, statistical methods.
Even more precision: higher-order SMEFT, in loops and in operator dimension
SMEFT at dim-6: RGE evolution, operator mixing and their impact in global fits
SMEFT at dim-8: motivation, technical challenges and case studies
CFU / Hours
2 ECTS
Teaching period
The course will be given in a blended form, with lectures given in presence and with remote connection at the same time.
Each chapter of the course will feature front lectures and exercises.
The experimental aspects will be discussed on Oct. 10 and 11, while the theory part will be disussed on Oct. 28 and 29.
The detailed timetable will be published soon.