The S-matrix bootstrap with neural optimizers. Part I. Zero double discontinuity
Abstract In this work, we develop machine learning techniques to study nonperturbative scattering amplitudes. We focus on the two-to-two scattering amplitude of identical scalar particles and study a toy model where we set the double discontinuity to zero, thereby simplifying the problem numerically...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP07(2025)210 |
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| Summary: | Abstract In this work, we develop machine learning techniques to study nonperturbative scattering amplitudes. We focus on the two-to-two scattering amplitude of identical scalar particles and study a toy model where we set the double discontinuity to zero, thereby simplifying the problem numerically. Neural networks provide an efficient parameterization for scattering amplitudes, offering a flexible toolkit to describe the details of their nonperturbative structure. Combined with the bootstrap approach based on the dispersive representation of the amplitude and machine learning’s gradient descent algorithms, they offer a new method to explore the space of consistent S-matrices. We derive bounds on the values of the first two low-energy Taylor coefficients of the amplitude and characterize the resulting amplitudes that populate the allowed region. Crucially, we parallel our neural network analysis with the standard S-matrix bootstrap, both primal and dual, and observe perfect agreement across all approaches. |
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| ISSN: | 1029-8479 |