标题： 贝叶斯最小二乘反褶积 显示英文标题

标题： Bayesian least squares deconvolution

摘要： 目标。 开发一种完全基于贝叶斯的最小二乘去卷积（LSD）方法，可用于利用多线技术在噪声受限的恒星光谱极化观测中可靠地检测磁信号。 方法。 我们在贝叶斯框架下考虑LSD，并引入了灵活的高斯过程（GP）先验用于LSD轮廓。此先验允许结果自动适应信号的存在。我们利用多个线性代数恒等式来加速计算。最终算法可以在几秒钟内处理数千条光谱线。 结果。 我们通过合成实验验证了该方法的可靠性，并将其应用于真实磁星的光谱极化观测。我们能够使用少量光谱线恢复磁信号，同时获得每个速度区间上的不确定性。这使得用户可以判断检测到的信号是否可靠。用于计算贝叶斯LSD轮廓的代码是免费提供的。 显示英文摘要

摘要： Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the Bayesian framework and we introduce a flexible Gaussian Process (GP) prior for the LSD profile. This prior allows the result to automatically adapt to the presence of signal. We exploit several linear algebra identities to accelerate the calculations. The final algorithm can deal with thousands of spectral lines in a few seconds. Results. We demonstrate the reliability of the method with synthetic experiments and we apply it to real spectropolarimetric observations of magnetic stars. We are able to recover the magnetic signals using a small number of spectral lines, together with the uncertainty at each velocity bin. This allows the user to consider if the detected signal is reliable. The code to compute the Bayesian LSD profile is freely available.