标题： 使用机器学习的辐射计校准 显示英文标题

标题： Radiometer Calibration using Machine Learning

摘要： 辐射计是射电天文学中的关键仪器，几乎构成了所有射电望远镜的主要组成部分。它们测量电磁辐射的强度，并将这种辐射转换为电信号。辐射计的主要部件包括天线和低噪声放大器（LNA），后者是“接收机”链的核心部分。接收机引入的仪器效应通常会在校准过程中被修正或消除。然而，天线与接收机之间的阻抗失配可能会引入不需要的信号反射和畸变。 传统的校准方法，例如 迪基切换（Dicke switching），通过在天线和已知特征参考源之间交替切换接收机输入，以比较的方式减轻误差。 机器学习（ML）领域的最新进展提供了有前景的替代方案。 使用已知信号源训练的神经网络提供了一种强大的手段，可以建模和校准传统解析方法难以应对的复杂系统。 这些方法对于探测高红移处原子氢的天空平均21厘米信号尤为重要。 这是今天观测宇宙学面临的主要挑战之一。 在这里，我们首次引入并测试了一种基于机器学习的校准框架，该框架能够达到辐射计量实验所需的精度，以探测21厘米谱线。 显示英文摘要

摘要： Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.