发表机构
National Radio Astronomy Observatory; NSF–Simons AI Institute for Cosmic Origins; University of Utah; Joint ALMA Observatory（国家射电天文台; NSF-Simons宇宙起源人工智能研究所; 犹他大学; ALMA联合观测站）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对射电干涉观测中带通校准异常检测，提出基于 XGBoost 和扫描统计特征的分类器框架，在 ALMA 数据上显著降低误分类率，可扩展至下一代设施。
AI 中文摘要
带通校准可校正干涉射电观测中跨观测频率的仪器响应，因此测得的校准解固有地包含随仪器配置和噪声条件变化的频谱变化。然而，某些信号包含异常，若未能捕获，可能导致构建的科学产品质量不佳。天文台流水线通常使用具有单个线性决策边界的标量指标。对于参数空间相对有限的观测，构建简单参数模型可能有效，但随着参数空间扩大，其准确性逐渐降低。为应对这一挑战，我们设计了一个机器学习框架，将异常识别视为监督分类任务，并学习跨多个特征的复杂非线性决策边界，以优化预测性能。具体而言，我们使用一个基于多个特征和专门扫描统计模型的 XGBoost 模型，对 ALMA 带通校准的幅度解中的异常进行分类。该分类器框架在广泛且质量受控的 ALMA 数据集上训练，利用基于核的扫描统计导出的特征来表征偏差频谱区间，同时保持可扩展至其他特征集。与当前流水线启发式方法相比，该方法大幅减少了带通异常检测中的误分类。通过改进标记决策和校准质量，该分类器框架为天文台运行提供了可扩展的补充，并可推广至下一代射电设施。
英文摘要
Bandpass calibration corrects instrumental responses across observing frequencies in interferometric radio observations, so the measured calibration solutions inherently contain spectral variation that varies with instrumental configurations and noise conditions. However, some signals contain anomalies, which if not caught can lead to poorly constructed science products. Observatory pipelines often use scalar metrics with individual, linear decision boundaries. Constructing a simple parametric model can be effective for observations with a relatively limited parameter space, but it becomes progressively less accurate as the parameter space expands. To address this challenge, we design a machine learning framework that treats anomaly identification as a supervised classification task and learns complex, non-linear decision boundaries across multiple features optimized for predictive performance. In particular, we use an XGBoost model that sits on top of several features and specialized scan statistic models to classify anomalies in amplitude solutions from ALMA bandpass calibrations. Trained on an extensive, quality-controlled ALMA dataset, this classifier framework leverages features derived from kernel-based scan statistics to characterize deviating spectral intervals, while remaining extensible to additional feature sets. This method substantially reduces misclassifications in bandpass anomaly detection compared to the current pipeline heuristics. By improving flagging decisions and calibration quality, this classifier framework provides a scalable supplement for observatory operations and is generalizable to next-generation radio facilities.
Comments14 pages, 7 figures
