佩恩零项目I：数秒内基于物理模型的恒星光谱
The Payne Zero Project I: Stellar Spectra from Physical Models in Seconds
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中文总结 AI 辅助
研究针对现代恒星巡天中生成光谱耗时久的问题，提出佩恩零项目，通过重组计算用于GPU原生合成等，大幅提升光谱生成速度，能直接进行多元素拟合和原子数据校准至巡天规模。
中文摘要 AI 辅助
现代恒星巡天能测量数百万条光谱，但生成一个自洽的大气和光谱可能需要数十分钟。这促使了网格、光谱模拟器和数据驱动模型的发展。我们提出了佩恩零，它对一维局部热动平衡库鲁茨计算进行重组，用于GPU原生合成和多核大气迭代，并与原始Fortran程序进行验证。在NVIDIA H100 GPU上，300 - 1000nm采样率为$R_{\rm grid}=300{,}000$的太阳光谱约需14秒，APOGEE的1500 - 1700nm区间约需1秒。在16个AMD CPU线程上，物理大气迭代需2 - 5秒，学习初始化器减少了收敛所需的迭代次数。最终光谱在测试的矮星和巨星区域保持实际等效。这些速度使得直接合成可置于优化器内而无需标签到通量的光谱模拟器。我们展示了对简化的APOGEE光谱进行直接多元素拟合，并恢复了与巡天目录大致一致的多元素丰度趋势。GPU驻留的速度偏移、展宽、线扩散函数卷积和探测器采样相对于合成成本可忽略不计。在H100上，直接合成搜索每颗恒星不到一分钟，而大气验证在多核CPU上独立运行。相同的计算图在H100上约一分钟内可联合校准超过$10^5$个振子强度和阻尼校正。因此，佩恩零将直接物理拟合和原子数据校准提升到了巡天规模。代码可在该https URL获取。
英文摘要
Modern stellar surveys measure millions of spectra, yet one self-consistent atmosphere and spectrum can require tens of minutes. This cost has motivated grids, spectral emulators, and data-driven models. We present Payne Zero, which reorganizes one-dimensional LTE Kurucz calculations for GPU-native synthesis and multicore atmosphere iteration, and validate it against the original Fortran programs. A 300-1000 nm solar spectrum sampled at a grid resolution of R = 300,000 takes about 14 s on an NVIDIA H100 GPU, while the APOGEE 1500-1700 nm interval takes about 1 s. Physical atmosphere iterations take 2-5 s on 16 AMD CPU threads, and learned initializers reduce the iterations required for convergence. Final spectra remain in practical parity across the tested dwarf and giant regimes. These speeds place direct synthesis inside an optimizer without a label-to-flux spectral emulator. We demonstrate direct many-element fitting of reduced APOGEE spectra and recover multi-element abundance trends broadly consistent with the survey catalog. GPU-resident velocity shifts, broadening, line-spread-function convolution, and detector sampling add negligible cost relative to synthesis. The direct-synthesis search takes less than one minute per star on an H100, while atmosphere verification runs independently on multicore CPUs. The same computational graph calibrates more than 100,000 oscillator-strength and damping corrections jointly against the Sun and Arcturus in about one minute on an H100. Payne Zero therefore brings direct physical fitting and atomic-data calibration to survey scale. The code is available at https://github.com/tingyuansen/payne-zero.
发表机构
- The Ohio State University（俄亥俄州立大学）
- Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）
- Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。
