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Communication Dans Un Congrès Année : 2021

A Machine Learning Technique for Dynamic Aperture Computation

Résumé

Currently, dynamic aperture calculations of high-energy hadron colliders are performed through computer simulations, which are both a resource-heavy and time-costly processes. The aim of this study is to use a reservoir computing machine learning model in order to achieve a faster extrapolation of dynamic aperture values. A recurrent echo-state network (ESN) architecture is used as a basis for this work. Recurrent networks are better fitted to extrapolation tasks while the reservoir echo-state structure is computationally effective. Model training and validation is conducted on a set of "seeds" corresponding to the simulation results of different machine configurations. Adjustments in the model architecture, manual metric and data selection, hyper-parameters tuning and the introduction of new parameters enabled the model to reliably achieve good performance on examining testing sets.
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Dates et versions

hal-03560009 , version 1 (07-02-2022)

Identifiants

Citer

Barbara Dalena, Mehdi Ben Ghali. A Machine Learning Technique for Dynamic Aperture Computation. 12th International Particle Accelerator Conference , May 2021, Online, Brazil. pp.THPAB201, ⟨10.18429/JACoW-IPAC2021-THPAB201⟩. ⟨hal-03560009⟩
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