发表机构
Aix Marseille Univ. CNRS, CNES, LAM; Università degli Studi di Milano-Bicocca; National Centre for Nuclear Research; INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna; INAF - Osservatorio Astronomico di Trieste; INAF - Osservatorio Astronomico di Brera; Kapteyn Astronomical Institute, University of Groningen; Durham University; IUCAA（艾克斯马赛大学; 米兰比可卡大学; 国家核研究中心; 博洛尼亚天文物理与空间科学研究所; 的里雅斯特天文台; 布雷拉天文台; 格罗宁根大学卡普坦天文研究所; 杜伦大学; 印度联合天体物理研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于U-Net的神经网络方法，用于在类星体光谱中检测金属吸收线，在信噪比约为4时达到约90%的完整度、纯度和F1分数，适用于当前和未来大型光谱巡天。
AI 中文摘要
当前和未来的大型光谱巡天正在显著增加所观测类星体光谱的数量和分辨率，这要求开发高效且精确的自动化技术来检测吸收特征。本研究聚焦于使用一种新颖的U-Net模型，在类星体静止系波长区间 $1230\\,\mathring{\mathrm{A}} \leq λ_{\rm RF} \leq 3095\\,\mathring{\mathrm{A}}$ 内的WEAVE模拟光谱中检测金属吸收特征。我们测试了该网络在理想数据上以及模拟了实际数据中连续谱拟合步骤后的吸收检测性能。这些架构的性能通过吸收线在信噪比（$\mathrm{S}/ \mathrm{N}_\mathrm{line} $）分箱中的完整度、纯度和F1分数以及连续谱的绝对分数流量误差来评估。同时研究了恢复正确线中心的能力。在 $\mathrm{S}/ \mathrm{N}_\mathrm{line} \approx 4$ 时，U-Net在所有指标（完整度、纯度和F1分数）上均达到约90%的分数。所有 $\mathrm{S}/ \mathrm{N}_\mathrm{line} \geq 5$ 的假阳性检测都落在阻尼Ly$α$系统的宽Ly$α$吸收体分布的尾部。连续谱拟合与线检测的组合在 $\mathrm{S}/ \mathrm{N}_\mathrm{line} \geq 4$ 时对检测性能的影响可忽略不计。我们提出的U-Net架构为当前和即将开展的大型光谱巡天中吸收线的分析提供了一个有竞争力的工具，并且非常适合识别任何吸收线特征。
英文摘要
Current and future large spectroscopic surveys are significantly enhancing the volume and resolution of quasar spectra that are observed, which requires the creation of efficient and precise automated techniques to detect absorption features. This study focuses on the detection of metal absorption features using a novel U-Net model on WEAVE-like mock spectra in the quasar rest-frame wavelength interval $1230\,\mathring{\mathrm{A}} \leq λ_{\rm RF} \leq 3095\,\mathring{\mathrm{A}}$. We test the network performance for absorption detection both on ideal data and after simulating the continuum fitting step as applied on real data. The performance of these architectures is evaluated by the completeness, purity, and F1 score reached in bins of signal-to-noise ($\mathrm{S}/ \mathrm{N}_\mathrm{line} $) for the absorption lines and with the absolute fractional flux error for the continuum. The ability to recover the correct line centers is also studied. The U-Net reaches scores of $\approx 90\%$ for all metrics (completeness, purity, and F1 score) at $\mathrm{S}/ \mathrm{N}_\mathrm{line} \approx 4$. All false positive detections with $\mathrm{S}/ \mathrm{N}_\mathrm{line} \geq 5$ fall in the tails of the broad Ly$α$ absorbers distribution of damped Ly$α$ systems. The combination of continuum fitting and line detections has negligible effects on the detection performance at $\mathrm{S}/ \mathrm{N}_\mathrm{line} \geq 4$. Our proposed U-Net architecture offers a competitive tool for the analysis of absorption lines in current and upcoming large spectroscopic surveys and is well-suited to the identification of any absorption line feature.
Comments13 pages, 9 figures. Accepted in A&A
