Speaker
Description
Recent advances in machine learning have given rise to a multitude of applications in physics, from jet tagging algorithms, to fast detector simulators or AI-driven symbolic regression and give us the perfect tool for tackling hard numerical problems for which classical algorithms are challenging to design. Is it well known that neural networks are universal function approximators and as such are ideal candidates for solving integro-differential equations.
The goal of this project is to apply these techniques to the study of relativistic scattering of particles. We combine the nonperturbative S-matrix bootstrap methods with the modern AI tools to study the fundamental aspects of particle scattering which is not accessible using conventional computational methods, such as lattice or Feynman diagrams.
This project builds on the unique expertise of the PI on the question of the bootstrap methods. After years of conceptual development of a new method [1,2] which constitutes the only proposal to produce fully consistent scattering amplitudes, it was realised that machine-learning methods are exactly suited to tackle this problem numerically [3,4,5].
The pilot studies [4,5] in particular show the applicability of machine-learning methods for the specific problem at hand. In some work in progress [5] with the same group, the PI managed to obtain the first strongly coupled scattering amplitudes ever, in some idealised scenario of certain simple scalar particles.
The general goal of the project would be to systematically apply the method to various theories, and focus on theories with experimental relevance such as QCD and the strong interactions, and also gravitationnal theories. It will bring new key insights on the 60 year old question of the Froissart bound saturation in scattering experiments, produce state of the art models for scattering amplitudes between scalar mesons in QCD and open a new avenue of research to study quantum gravity at high energies.
The funding would come at a particularly timely moment in the development: the method exists and crucially needs workpower to be applied to relevant cases.
[1] Scattering amplitudes from dispersive iterations of unitarity
P. Tourkine, A. Zhiboedov
[arXiv:2303.08839] JHEP 11 (2023) 005
[2] Scattering from production in 2d
P. Tourkine, A. Zhiboedov
[arXiv:2101.05211] JHEP 07 (2021) 228
[3] Reconstructing S-matrix Phases with Machine Learning,
A. Dersy, M. D. Schwartz, A. Zhiboedov
[arXiv:2308.09451] JHEP 05 (2024) 200
[4] The S-matrix bootstrap with neural optimizers. Part I. Zero double discontinuity
Mehmet Asim Gumus, Damien Leflot, Piotr Tourkine, Alexander Zhiboedov,
JHEP 07 (2025) 210 [arXiv: 2412.09610]
[5] The S-matrix bootstrap with neural optimizers. Part II. Full problem with one subtraction
Mehmet Asim Gumus, Damien Leflot, Piotr Tourkine, Alexander Zhiboedov,
To be published (expected Oct. or Nov. 2025)
CERN group/ Experiment
Theory
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | See the comment above |
| Timeline | 3 years |
| Available person power | 3 (2 faculty, 1 PhD student) |
| Additional person power request | 1 CERN TH-AI research fellow |
| Indicative hardware resources needs | The work that is currently done uses 4 H100 GPUs. |