标题： 基于JAX的信息场理论从天线信号轨迹中推断空气簇射电流 显示英文标题

标题： Information Field Theory with JAX infers Air Shower Electric Currents from Antenna Signal Traces

摘要： 在白天直接成像宇宙射线引起的粒子级联是天体粒子物理中的一个长期挑战。 捕捉这些级联图像的一个有希望的途径是通过其带电粒子产生的无线电波发射。 它们相应的电流矢量随着时间演变，随着粒子级联穿过地球大气层，导致天线阵列中的特征时间依赖电场。 在这项工作中，我们利用Python工具包数值信息场理论NIFTy中的现代贝叶斯推断技术，并结合Python库JAX的高性能数值计算能力。 这种创新的组合使我们能够从地面天线阵列收集的数据中重建粒子级联及其时间发展。 我们的方法为详细成像宇宙射线级联开辟了一条初步路径，可能有助于加深对高能天体物理过程的理解。 显示英文摘要

摘要： Direct imaging of cosmic-ray-induced particle showers during daylight is a long-standing challenge in astroparticle physics. A promising avenue for capturing images of these showers is through the radio emissions generated by their electrically charged particles. Their corresponding current vectors evolve over time as the particle shower propagates through the Earth's atmosphere leading to a characteristic time-dependent electric field in an antenna array. In this work, we harness modern Bayesian inference techniques within the Python toolkit for numerical information field theory NIFTy, coupled with the high-performance numerical computing capabilities of the Python library JAX. This innovative combination enables us to reconstruct the particle shower and its temporal development from data collected by a ground-based antenna array. Our approach opens an initial pathway for detailed imaging of cosmic-ray showers, potentially advancing our understanding of high-energy astrophysical processes.