标题： AMBER：具有旋转不变性的多路复用光谱仪背景估计算法 显示英文标题

标题： AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence

摘要： 最先进的中子谱仪能够同时测量高维数据集，从而实现动态材料特性的大量采集率。 在本文中，我们提出了旋转不变的多路复用谱仪背景估计算法（AMBER），这是一种分割算法，旨在将测得的中子散射数据分解为与模型无关的前景和背景贡献。 该方法利用了在数据收集过程中同时测量背景和前景信号的事实，依赖于背景贡献的旋转不变性。 该算法最初是为多路复用中子谱仪开发的，旨在大幅减少耗时的专业输入，从而促进完整数据集的使用，同时最小化系统误差的来源。 显示英文摘要

摘要： State-of-the art neutron spectrometers enable simultaneous measurements of high-dimensional datasets, allowing for a large collection rate of dynamic material properties. In this paper, we present the Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence (AMBER), which is a segmentation algorithm designed to decompose measured neutron scattering data into model-agnostic foreground and background contributions. The method takes advantage of the fact that background and foreground signals are measured simultaneously during the data collection process, relying on rotational independence of background contributions. The algorithm, initially developed for multiplexing neutron spectrometers, aims to strongly reduce time consuming expert input, therefore promoting full data set usage while minimizing the source of systematic errors.