We present an end-to-end pipeline for estimating stellar parameters from Sloan Digital Sky Survey Data Release 12 spectra using a fully connected multitask neural network with residual blocks, whose hyperparameters are tuned via Bayesian optimization. The preprocessing pipeline includes per-spectrum standardization, RobustScaler normalization of the target variables -- effective temperature $T_{\mathrm{eff}}$, metallicity $[\mathrm{Fe/H}]$, and surface gravity $\log g$ -- and data augmentation via Gaussian noise injection. On a held-out test set, the model achieved Mean Absolute Errors (MAE) of $59.76~\mathrm{K}$ for $T_{\mathrm{eff}}$, $0.103~\mathrm{dex}$ for $[\mathrm{Fe/H}]$, and $0.130~\mathrm{dex}$ for $\log g$. Normalized against the full-scale range of each parameter, these results represent range-normalized errors between $1\%$ and $3\%$, achieved with a highly efficient model complexity of approximately 540,000 trainable parameters. These results demonstrate that a compact residual multitask architecture, combined with principled signal preprocessing, provides a parameter-efficient solution for nonlinear parameter estimation in large-scale spectral datasets. In particular, the proposed model achieves competitive performance with substantially lower complexity than deeper neural network baselines.

翻译：我们提出了一种端到端的流水线，用于从斯隆数字巡天数据发布12期的光谱中估计恒星参数。该方法采用带有残差块的全连接多任务神经网络，其超参数通过贝叶斯优化进行调优。预处理流程包括：每条光谱的标准化处理、目标变量（有效温度$T_{\mathrm{eff}}$、金属丰度$[\mathrm{Fe/H}]$和表面重力$\log g$）的RobustScaler归一化，以及通过高斯噪声注入进行数据增强。在独立测试集上，该模型获得的平均绝对误差（MAE）为：$T_{\mathrm{eff}}$为$59.76~\mathrm{K}$，$[\mathrm{Fe/H}]$为$0.103~\mathrm{dex}$，$\log g$为$0.130~\mathrm{dex}$。这些结果经每个参数全量程归一化后，对应的范围归一化误差介于$1\%$至$3\%$之间，而模型仅需约54万个可训练参数，具有极高的效率。这些结果表明，紧凑的残差多任务架构与合理的信号预处理相结合，为大尺度光谱数据集中的非线性参数估计提供了一种参数高效的解决方案。特别地，与更深层的神经网络基线相比，所提模型以显著更低的复杂度实现了具有竞争力的性能。