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arXiv 2406.04248astro-ph.SRastro-ph.IM
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一种用于白矮星低质量光谱数据的自适应参数估计器

An adaptive parameter estimator for poor-quality spectral data of white dwarfs

Duo Xie, Jiangchuan Zhang, Yude Bu, Zhenping Yi, Meng Liu, Xiaoming Kong

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AI总结：

本文提出EstNet算法，融合残差网络、注意力机制和门控循环单元等技术，在低信噪比白矮星光谱数据上实现高精度参数估计，性能优于主流方法。

摘要：

白矮星代表了97%恒星的最终演化阶段，因此精确的参数测量对于理解恒星演化至关重要。传统的估计方法需要拟合光谱或测光数据，这要求高质量的数据。近年来，机器学习因其速度、自动化和准确性，在光谱数据处理中发挥了关键作用。然而，目前存在两个常见问题。首先，大多数研究依赖于高信噪比（SNR > 10）的数据，导致许多低质量数据集未被充分利用。其次，现有的机器学习模型主要基于卷积网络、循环网络及其变体，无法同时捕获光谱的空间信息和序列信息。为了解决这些挑战，我们设计了Estimator Network（EstNet），这是一种集成了多种技术的先进算法，包括Residual Networks、Squeeze and Excitation Attention、Gated Recurrent Units、Adaptive Loss和Monte-Carlo Dropout Layers。我们对5,965条低质量白矮星光谱（R~1800，SNR~1.17）进行了参数估计，有效温度的平均百分比误差为14.86%，表面重力为3.97%。这些结果显著优于其他主流算法，并与传统理论光谱拟合方法的结果一致。未来，我们的算法将应用于中国空间站望远镜和大型综合巡天望远镜的大规模参数估计。

英文摘要：

White dwarfs represent the end stage for 97% of stars, making precise parameter measurement crucial for understanding stellar evolution. Traditional estimation methods involve fitting spectra or photometry, which require high-quality data. In recent years, machine learning has played a crucial role in processing spectral data due to its speed, automation, and accuracy. However, two common issues have been identified. First, most studies rely on data with high signal-to-noise ratios (SNR > 10), leaving many poor-quality datasets underutilized. Second, existing machine learning models, primarily based on convolutional networks, recurrent networks, and their variants, cannot simultaneously capture both the spatial and sequential information of spectra. To address these challenges, we designed the Estimator Network (EstNet), an advanced algorithm integrating multiple techniques, including Residual Networks, Squeeze and Excitation Attention, Gated Recurrent Units, Adaptive Loss, and Monte-Carlo Dropout Layers. We conducted parameter estimation on 5,965 poor-quality white dwarf spectra (R~1800, SNR~1.17), achieving average percentage errors of 14.86% for effective temperature and 3.97% for surface gravity. These results are significantly superior to other mainstream algorithms and consistent with the outcomes of traditional theoretical spectrum fitting methods. In the future, our algorithms will be applied for large-scale parameter estimation on the Chinese Space Station Telescope and the Large Synoptic Survey Telescope.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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