Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to moderate-resolution surveys. We investigate this problem using pre-trained models, focusing on simple neural networks such as multilayer perceptrons (MLPs), with a case study transferring from LAMOST low-resolution spectra (LRS) to DESI medium-resolution spectra (MRS). Specifically, we pre-train MLPs on either LRS or their embeddings and fine-tune them for application to DESI stellar spectra. We compare MLPs trained directly on spectra with those trained on embeddings derived from transformer-based models (self-supervised foundation models pre-trained for multiple downstream tasks). We also evaluate different fine-tuning strategies, including residual-head adapters, LoRA, and full fine-tuning. We find that MLPs pre-trained on LAMOST LRS achieve strong performance, even without fine-tuning, and that modest fine-tuning with DESI spectra further improves the results. For iron abundance, embeddings from a transformer-based model yield advantages in the metal-rich ([Fe/H] > -1.0) regime, but underperform in the metal-poor regime compared to MLPs trained directly on LRS. We also show that the optimal fine-tuning strategy depends on the specific stellar parameter under consideration. These results highlight that simple pre-trained MLPs can provide competitive cross-survey generalization, while the role of spectral foundation models for cross-survey stellar parameter estimation requires further exploration.

翻译：跨巡天泛化是恒星光谱分析中的一个关键挑战，特别是在从低分辨率巡天向中分辨率巡天迁移的情况下。我们使用预训练模型研究此问题，重点关注多层感知机（MLP）等简单神经网络，并以从LAMOST低分辨率光谱（LRS）迁移至DESI中分辨率光谱（MRS）为例进行案例研究。具体而言，我们在LRS或其嵌入表示上预训练MLP，然后对其进行微调以应用于DESI恒星光谱。我们比较了直接在光谱上训练的MLP与在基于Transformer的模型（为多个下游任务预训练的自监督基础模型）生成的嵌入表示上训练的MLP。我们还评估了不同的微调策略，包括残差头适配器、LoRA和全参数微调。我们发现，在LAMOST LRS上预训练的MLP即使不进行微调也能取得强劲性能，而使用DESI光谱进行适度微调可进一步改善结果。对于铁丰度，基于Transformer模型生成的嵌入表示在富金属（[Fe/H] > -1.0）区间具有优势，但在贫金属区间表现不及直接在LRS上训练的MLP。我们还表明，最优微调策略取决于所研究的特定恒星参数。这些结果突显了简单的预训练MLP能够提供具有竞争力的跨巡天泛化能力，而光谱基础模型在跨巡天恒星参数估计中的作用仍需进一步探索。