发表机构
The Ohio State University; Max-Planck-Institut für Astronomie（俄亥俄州立大学; 马克斯·普朗克天文学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Armillary利用Wasserstein距离和最近邻图，从光谱自身构建多维坐标，实现无标签的恒星参数估计，在APOGEE和DESI数据上优于传统降维方法，精度与自编码器相当。
AI 中文摘要
恒星光谱由少数几个参数决定，因此一组光谱位于以这些参数为坐标的低维流形上。我们引入了Armillary，它将Baron和Ménard的Sequencer从排序推广到多维坐标，仅由光谱本身构建。两颗恒星通过其有符号吸收剖面之间的Wasserstein距离进行比较。通过最近邻图中的最短路径测量沿流形的距离并给出坐标，同一图将少数恒星的标签传递给所有其他恒星。这些坐标在$T_{\rm eff}$和$\log g$上比主成分、t-SNE、UMAP或自编码器更接近线性。标签传递无需深度学习，比前三种方法更精确，与自编码器相当。在202,932颗APOGEE DR17恒星上，抽取32颗训练星即可恢复保留的矮星和巨星ASPCAP的$T_{\rm eff}$、$\log g$和[M/H]，1σ误差分别为100 K、0.18和0.083 dex。分辨盘星中巨星的[α/M]两条α序列需要128颗训练星，而使用2048颗时四个误差分别改善到33 K、0.06、0.026和0.022 dex。在104,799颗DESI恒星上，来自APOGEE的32个训练标签恢复恒星参数到129 K、0.25和0.203 dex，即使光谱退化到信噪比约20。在信噪比约100时，2048个训练标签分辨DESI巨星中盘星的两条α序列，与APOGEE的误差为51 K、0.07、0.047和0.026 dex。代码公开于此https URL。
英文摘要
A stellar spectrum is determined by a few parameters, so a set of spectra lies on a low-dimensional manifold with those parameters as its coordinates. We introduce Armillary, which generalises the Sequencer of Baron and Ménard from an ordering to multidimensional coordinates, constructed from the spectra alone. Two stars are compared by the Wasserstein distance between their signed absorption profiles. Shortest paths through a nearest-neighbour graph measure distance along the manifold and give the coordinates, and the same graph transfers the labels of a few stars to all the others. The coordinates are more nearly linear in $T_{\rm eff}$ and $\log g$ than principal components, t-SNE, UMAP or an autoencoder. The label transfer, without deep learning, is more precise than the first three and comparable to the autoencoder. On 202,932 APOGEE DR17 stars, an illustrative draw of 32 training stars recovers the ASPCAP $T_{\rm eff}$, $\log g$ and [M/H] of held-out dwarfs and giants, with $1σ$ errors of 100 K, 0.18 and 0.083 dex. Resolving the two $α$ sequences of the disc in [$α$/M] for giants takes 128 training stars, and with 2048 the four errors improve to 33 K, 0.06, 0.026 and 0.022 dex. On 104,799 DESI stars, 32 training labels from APOGEE recover the stellar parameters to 129 K, 0.25 and 0.203 dex, even for spectra degraded to a signal-to-noise of about 20. At a signal-to-noise of about 100, 2048 training labels resolve the two $α$ sequences of the disc in DESI giants, with errors against APOGEE of 51 K, 0.07, 0.047 and 0.026 dex. The code is public at https://github.com/tingyuansen/armillary.
Comments24 pages, 11 figures; submitted to the Open Journal of Astrophysics. Comments welcome
