Introduction

• Florian Bernlochner (uni-bonn)

High dimensional (SM)EFT fits with large number of free parameters

• Philip Bechtle (uni-bonn)

High dimensional (SM)EFT fits with large number of free parameters. Overarching goal: "No Lose theorem" for future colliders: Which combination of deviations from the SM aty low energies (gmin2, etc) would guarantee a statistically significant distortion of spectrums (or more clear signals) at (a) future collider(s)

How to determine or even define "intrinsic" systematic uncertainties of Neural Network based classifiers

• Timo Saala

How to determine or even define "intrinsic" systematic uncertainties of Neural Network based classifiers. One approach might be adversarial attacks, which are studied within the AISafety Project of BMBF

"TH-EXP-synergies: how to efficiently scan SMEFT-couplings in high-dimensional spaces"

• Lara Nollen

EFT strategies and synergies in global fits

Numerical methods for sampling of high-dimensional phase spaces

• Cornelius Grunwald

Coffee Break

Normalizing flows for lattice field theories

• Kim Nicoli (TRA Matter, HISKP, Bethe Center for Theorethical Physics)

Reconstruction of primary and secondary vertices with machine learning techniques. Exploring the reconstruction of long-lived particles.

• Vadim Kostyukhin

Higgs Tagging and Tau Identification with Computer Vision

• Tatjana Lenz (uni-bonn)

Challenges in LFT

• Oliver Witzel

Challenges in LFT: increase of computational power over network (communication avoiding algorithms, data compression); ergodicity for larger and finer QCD simulations; overcoming bulk lattice phases in BSM stimulations of very strong couplings

ML approaches to MC phase-space sampling

• Wolfgang Kilian

Performance-portability and task parallelism for lattice field theory

• Bartosz Kostrzewa

ML & experimental particle physics

• Johannes Albrecht