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5th General Meeting of the LHC EFT Working Group

2 December 2022
CERN
Europe/Zurich timezone

Conveners

Contribution List

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9. Introduction
Andrew Gilbert (Northwestern University (US))
02/12/2022, 09:30
10. Recent ATLAS EFT activities
Eleonora Rossi (University of Oxford (GB))
02/12/2022, 09:55
11. Recent CMS EFT activities
Sergio Sanchez Cruz (Universitaet Zuerich (CH))
02/12/2022, 10:20
12. Report on the ATLAS+CMS EFT fit exercise
Fabian Stager (University of Zurich (CH))
02/12/2022, 11:15
13. Additional proposal for the treatment of EFT truncation, validity and related uncertainties
Tim Cohen (CERN)
02/12/2022, 11:40
15. Report on BMS/EFT matching activities
Kristin Lohwasser (University of Sheffield (GB))
02/12/2022, 11:55
16. Report on experimental measurements and observables for EFT interpretations
Andrei Gritsan (Johns Hopkins University (US))
02/12/2022, 13:30
17. Machine learning observables in EFT interpretations
Robert Schoefbeck (Hephy Vienna)
02/12/2022, 13:55
18. Frameworks and tools for systematic data analysis reinterpretations
Lukas Alexander Heinrich (Technische Universitat Munchen (DE))
02/12/2022, 14:20
19. Helicity-free techniques for the reweighting of MC Samples
Olivier Mattelaer (UCLouvain)
02/12/2022, 14:45
25. Discussion about SMEFT and HEFT exemplified for HH production
Jannis Lang
02/12/2022, 15:41
38. EFT : Applicabilities and Viabilities
Tisa Biswas (University of Calcutta)
02/12/2022, 15:46
26. Area 1 targets
Ilaria Brivio (University of Zurich)
02/12/2022, 15:51
28. HWG activities for the database of SMEFT predictions
Mr Matthew Knight (Imperial College London)
02/12/2022, 16:11
29. Area 2 targets
Abideh Jafari (Deutsches Elektronen-Synchrotron (DE))
02/12/2022, 16:16
30. Unbinned multivariate observables for global SMEFT analyses from machine learning
Jaco ter Hoeve (Nikhef and VU Amsterdam)
02/12/2022, 16:41
31. Efficient interpolation and practical observables
Nick Smith (Fermi National Accelerator Lab. (US))
02/12/2022, 16:46
32. Area 3 targets
Anke Biekoetter (IPPP Durham)
02/12/2022, 16:51
33. Area 4 targets
Jacob Julian Kempster (University of Sussex (GB))
02/12/2022, 17:16
35. ATLAS+CMS EFT combination (top)
Kirill Skovpen (Ghent University (BE))
02/12/2022, 17:21
36. Future EFT plans from the SMP CMS perspective
Matteo Presilla (Istituto Nazionale di Fisica Nucleare)
02/12/2022, 17:31
34. Area 5 targets
Kristin Lohwasser (University of Sheffield (GB))
02/12/2022, 18:01
37. Area 6 targets
Kristin Lohwasser (University of Sheffield (GB))
02/12/2022, 18:16
27. Area 2 (Chair: Robert Schoefbeck)
20. ATLAS-CMS EFT combination
Kirill Skovpen (Ghent University (BE))

Combination of EFT results from ATLAS and CMS requires a good understanding of underlying physics processes and systematics treatment. We, in the LHCtopWG, explore the fit models that are used in both experiments to approach the EFT combination by preserving a full-likelihood information in the fit. Preliminary results with the first comparisons are presented together with practical challenges...
22. Discussion about SMEFT and HEFT exemplified for Higgs boson pair production
Jannis Lang

We briefly motivate the linear (SMEFT) and non-linear (HEFT) bottom-up EFT extensions of the Standard Model and demonstrate the difference in organizing the theory for the example of Higgs boson pair production at NLO QCD. Comparing $m_{hh}$ distributions for HEFT and SMEFT for different truncation options of dimension 6 operator contributions, we point out to the pitfalls of a naive...
7. Efficient interpolation and practical observables
Nick Smith (Fermi National Accelerator Lab. (US))

For binned likelihood fits, a (dimension-6) quadratic EFT parameterization of the bin yield can be represented as a matrix norm. Often, this matrix is low-rank, presenting a possibility for more efficient computation in large fits. Analyzing the eigenspectrum of this matrix may also provide a path towards incremental optimization of binning, providing an intermediate approach to EFT analysis...
14. Impact of flavour assumptions on EFT interpretations
Ilaria Brivio (University of Zurich)
8. Introduction
Andrew Gilbert (Northwestern University (US))
4. Unbinned multivariate observables for global SMEFT analyses from machine learning
Maeve Madigan

Global determinations of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT) involve the inference of multiple parameters from a global dataset, and are often based on reinterpreting existing binned LHC measurements within the SMEFT framework. Based on our recently developed open-source framework, ML4EFT, we propose a new methodology that can be adopted by experiments...
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