The discrete and stochastic nature of the processes in the strong-field quantum electrodynamics (SF-QED) regime distinguishes them from classical ones. An important approach to identifying the SF-QED features is through the interaction of extremely intense lasers with plasma. Here, we investigate the seeded QED cascades driven by two counter-propagating laser pulses in the background of residual gases in a vacuum chamber via numerical simulations. We focus on the statistical distributions of positron yields from repeated simulations under various conditions. By increasing the gas density, the positron yields become more deterministic. Although the distribution stems from both the quantum stochastic effects and the fluctuations of the environment, the quantum stochastic effects can be identified via the width of the distribution and the exceptional yields, both of which are higher than the quantum-averaged results. The proposed method provides a statistical approach to identifying the quantum stochastic signatures in SFQED processes using high-power lasers and residual gases in the vacuum chamber.

中文翻译：

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通过真空中激光驱动的残余气体识别种子 QED 级联中的量子效应

强场量子电动力学 (SF-QED) 体系中过程的离散和随机性质使其与经典过程区分开来。识别 SF-QED 特征的一个重要方法是通过极强激光与等离子体的相互作用。在这里，我们通过数值模拟研究了真空室中残留气体背景下由两个反向传播激光脉冲驱动的种子 QED 级联。我们重点关注各种条件下重复模拟的正电子产额的统计分布。通过增加气体密度，正电子产额变得更加确定。尽管该分布源于量子随机效应和环境波动，但可以通过分布的宽度和异常的产率来识别量子随机效应，这两者都高于量子平均结果。所提出的方法提供了一种统计方法来识别使用高功率激光和真空室中残留气体的 SFQED 过程中的量子随机特征。