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Statut |
Confirmé |
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Série |
IPHT-PHM |
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Domaines |
math-ph |
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Date |
Lundi 16 Septembre 2024 |
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Heure |
11:00 |
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Institut |
IPHT |
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Salle |
Salle Claude Itzykson, Bât. 774 |
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Nom de l'orateur |
Lance Dixon |
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Prenom de l'orateur |
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Addresse email de l'orateur |
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Institution de l'orateur |
SLAC |
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Titre |
?AI for Analytic Amplitudes? |
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Résumé |
> Scattering amplitudes at high loop orders are remarkably difficult for humans to compute. Can machines do any better? As a first exercise, we map a set of scattering amplitudes into a “language-like†representation using the symbol associated with multiple polylogarithms. Then we train a transformer-based model (think ChatGPT) to predict (integer) coefficients of “words†in the symbol. Such models can also learn correlations between coefficients at different loop orders. I also discuss the next phase(s) of this work, i.e. whether one can predict the next loop order from information gleaned at lower orders.
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Numéro de preprint arXiv |
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Commentaires |
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Fichiers attachés |
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