IPAC'24 - 15th International Particle Accelerator Conference

Enhancing plasma wakefield accelerator analysis through machine learning

TUPS49

21 May 2024, 16:00

2h

Blues (MCC Exhibit Hall A)

Poster Presentation MC6.D13 Machine Learning Tuesday Poster Session

Speaker

Dr Monika Yadav (University of California, Los Angeles)

Description

In this groundbreaking study, an advanced particle-in-cell (PIC) simulation code,QuickPIC, is used to explore beam physics within Plasma Wakefield Accelerators (PWFA). The primary aim is to comprehensively analyze beam distributions, particularly those exhibiting perturbations with significant instabilities. To connect simulated beam distributions to physical observables, the study uses cutting-edge neural networks. This research underscores the transformative potential of machine learning (ML) in unraveling PWFA complexities and enhancing our capabilities in the development of advanced accelerators.

Funding Agency

This work was performed with the support of the US DOE, Division of HEP, under Contract No. DE-SC0009914, NSF PHY-1549132 CBB, DARPA under Contract N.HR001120C007.