Bayesian optimization of a laser-plasma accelerator aiming the production of high-energy electron beams for VHEE radiotherapy - Santos, Samara P.; Nunes, Bruno S.; Samad, Ricardo E.; V. Jr., Nilson D.; Alva-Sanchez, Mirko S.; Bonatto, Alexandre (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, 2025)
Radiation therapy aims to maximize tumor dose while minimizing exposure to healthy tissues. Despite advances, normal tissue toxicity remains a significant obstacle. While proton therapy shows promise, its high cost limits widespread adoption. Very high-energy electrons (VHEE, 50-250 MeV) represent an alternative with deep tissue penetration suitable for conventional and FLASH radiotherapy, which enables higher curative doses while reducing normal tissue toxicity. Laser-plasma accelerators (LPAs) offer a compact and costeffective approach to generating electron beams for VHEE radiotherapy, a modality with dosimetric advantages comparable to proton therapy but without requiring the large cyclotrons typically used for ion acceleration. This work implements Bayesian optimization to systematically tune particle-in-cell (PIC) simulations modeling an LPA, aiming to produce optimal electron beams for VHEE radiotherapy. Key findings include achieving a spectrum with two charge peaks at different energies and total integrated charge of 1.9 nC. Additionally, the Institute of Energy and Nuclear Research (IPEN) has recently received approval to acquire a 15 TW laser, making it the most powerful in the Southern Hemisphere. The framework developed in this work can be scaled to IPEN’s laser system parameters to guide future VHEE radiotherapy experiments.