To determine this limit, the researchers performed one- and two-dimensional particle-in-cell simulations on Skoltech’s Zhores supercomputer. The calculations were compared with a spectral model of wave propagation and a simplified analytical description.
As a result, the authors derived compact relationships linking pulse stretching to plasma density and thickness, as well as to the characteristic frequency of attosecond radiation. Based on these relationships, they formulated a criterion for accumulated dispersion that shows which target parameters preserve the attosecond structure of the pulse and which cause it to break down.
“An attosecond pulse can be compared to a very short flash needed to obtain a sharp image of an ultrafast process. When it passes through plasma that is too thick or too dense, the flash stretches and the ‘image’ loses sharpness. We have obtained a practical criterion that makes it possible to determine the permissible target parameters in advance and avoid a large number of computationally intensive trial-and-error simulations,” said Elizaveta Lipkova, the paper’s first author, a junior research engineer at the Skoltech AI Center’s Artificial Intelligence & Supercomputing Laboratory.
The paper also presents a map that can be used to determine the maximum target thickness permissible in attosecond experiments for a given plasma density. This diagram will help researchers select experimental parameters in advance and assess whether the pulse will retain the required duration after passing through the plasma.
The findings provide practical guidelines for developing more efficient plasma-based sources of attosecond ultraviolet and X-ray radiation, as well as components for attosecond optics. In the future, such systems could be used to investigate electron motion, chemical reactions, and material properties on extremely short timescales.
The authors of the paper are Elizaveta Lipkova, Junior Research Engineer at the Artificial Intelligence and Supercomputing Laboratory of the Skoltech AI Center; Dmitry Dylov, Professor and Head of the Computational Imaging Laboratory at the Skoltech AI Center; Sergey Rykovanov, Associate Professor and Head of the Artificial Intelligence and Supercomputing Laboratory at the Skoltech AI Center; and Jingwei Wang, Professor at the Shanghai Institute of Optics and Fine Mechanics of the Chinese Academy of Sciences.