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Title: Reconstructing S-matrix Phases with Machine Learning
A<sc>bstract</sc> An important element of theS-matrix bootstrap program is the relationship between the modulus of anS-matrix element and its phase. Unitarity relates them by an integral equation. Even in the simplest case of elastic scattering, this integral equation cannot be solved analytically and numerical approaches are required. We apply modern machine learning techniques to studying the unitarity constraint. We find that for a given modulus, when a phase exists it can generally be reconstructed to good accuracy with machine learning. Moreover, the loss of the reconstruction algorithm provides a good proxy for whether a given modulus can be consistent with unitarity at all. In addition, we study the question of whether multiple phases can be consistent with a single modulus, finding novel phase-ambiguous solutions. In particular, we find a new phase-ambiguous solution which pushes the known limit on such solutions significantly beyond the previous bound.  more » « less
Award ID(s):
2019786
PAR ID:
10555690
Author(s) / Creator(s):
; ;
Publisher / Repository:
Springer Nature
Date Published:
Journal Name:
Journal of High Energy Physics
Volume:
2024
ISSN:
1029-8479
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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