Bootstrapping string models with entanglement minimization and Machine-Learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910730490478592 |
|---|---|
| author | Bhat, Faizan Chowdhury, Debapriyo Saha, Arnab Priya Sinha, Aninda |
| author_facet | Bhat, Faizan Chowdhury, Debapriyo Saha, Arnab Priya Sinha, Aninda |
| contents | We present a new approach to bootstrapping string-like theories by exploiting a local crossing symmetric dispersion relation and field redefinition ambiguities. This approach enables us to use mass-level truncation and to go beyond the dual resonance hypothesis. We consider both open and closed strings, focusing mainly on open tree-level amplitudes with integer-spaced spectrum, and two leading Wilson coefficients as inputs. Using entanglement minimization in the form of the minimum of the first finite moment of linear entropy or entangling power, we get an excellent approximation to the superstring amplitudes, including the leading and sub-leading Regge trajectories. We find other interesting S-matrices which do not obey the duality hypothesis, but exhibit a transition from Regge behaviour to power law behaviour in the high energy limit. Finally, we also examine Machine-Learning techniques to do bootstrap and discuss potential advantages over the present approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bootstrapping string models with entanglement minimization and Machine-Learning Bhat, Faizan Chowdhury, Debapriyo Saha, Arnab Priya Sinha, Aninda High Energy Physics - Theory Mathematical Physics We present a new approach to bootstrapping string-like theories by exploiting a local crossing symmetric dispersion relation and field redefinition ambiguities. This approach enables us to use mass-level truncation and to go beyond the dual resonance hypothesis. We consider both open and closed strings, focusing mainly on open tree-level amplitudes with integer-spaced spectrum, and two leading Wilson coefficients as inputs. Using entanglement minimization in the form of the minimum of the first finite moment of linear entropy or entangling power, we get an excellent approximation to the superstring amplitudes, including the leading and sub-leading Regge trajectories. We find other interesting S-matrices which do not obey the duality hypothesis, but exhibit a transition from Regge behaviour to power law behaviour in the high energy limit. Finally, we also examine Machine-Learning techniques to do bootstrap and discuss potential advantages over the present approach. |
| title | Bootstrapping string models with entanglement minimization and Machine-Learning |
| topic | High Energy Physics - Theory Mathematical Physics |
| url | https://arxiv.org/abs/2409.18259 |