Description
Laser-plasma acceleration promises a next-generation compact source of electron beams for a large variety of applications. However, the non-linear, complex interaction between drive laser pulse and plasma results in a large parameter space, and tuning a plasma accelerator to produce useful electron beams is extremely complex. Here, we want to use machine learning techniques to autonomoulsy tune and optimize a laser-plasma accelerator, both in experiments and simulations. This project will include coding (in Python), running simulations (code: FBPIC) as well as hands-on work at the experiment (laser lab and accelerator tunnel).
Special Qualifications:
Knowledge in Python is expected. Experience with lasers is beneficial.
| Field | B4: Research on Accelerators |
|---|---|
| DESY Place | Hamburg |
| DESY Division | M |
| DESY Group | MLS |
Primary author
Andreas Maier
(MLS (Laser fuer Plasmabeschleunigung))
