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arXiv 2602.10330astro-ph.EPastro-ph.IMcs.LG
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利用神经网络高效减少恒星污染和噪声在行星透射光谱中的影响

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

SEAP/FACom, Instituto de Física - FCEN, Universidad de Antioquia（SEAP/FACom，物理研究所-FCEN，安提奥基亚大学）

David S. Duque-Castaño, Lauren Flor-Torres, Jorge I. Zuluaga

备注：
17 pages, 12 figures, 3 tables. Accepted by Astronomy & Astrophysics
发表信息：
A&A, 714 (2026)
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AI总结：

提出使用去噪自编码器（DAE）神经网络方法，从系外行星透射光谱中去除恒星污染和仪器噪声，在保持高精度的同时将计算成本降低一个数量级。

摘要：

背景：詹姆斯·韦伯太空望远镜（JWST）的红外灵敏度使透射光谱学达到前所未有的精度，彻底改变了系外行星大气表征。然而，恒星不均匀性（如黑子和光斑）仍然是主要的污染源，若未正确校正会导致大气反演偏差。目的：我们提出一种使用神经网络（特别是去噪自编码器DAE）减少系外行星透射光谱中恒星污染和仪器特定噪声的方法。目标是实现快速、准确的校正，提高大气参数反演的可靠性，并推广无监督算法用于高效数据处理。方法：我们使用大量类地行星（TRAPPIST-1e类似体）和亚海王星（K2-18b类似体）的合成数据集设计并训练了DAE架构。然后对污染光谱进行大气反演实验，以比较我们的深度学习方法与标准校正方法在精度和计算成本方面的表现。结果：我们的自编码器成功重建了无污染光谱，即使在低信噪比条件下也能保留关键分子特征。在反演测试中，与未校正观测相比，去噪自编码器预处理减少了反演丰度参数的偏差。值得注意的是，我们的方法在保持与同时拟合恒星污染相当的精度同时，计算成本低一个数量级。结论：这些结果表明，DAE在计算效率上优于传统校正方法，同时保持高精度，为其未来在岩石和巨行星大气表征管线中的集成铺平了道路。

英文摘要：

The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision. However, stellar heterogeneities (e.g., spots and faculae) remain a dominant source of contamination that can bias atmospheric retrievals if not properly corrected. We present a methodology for reducing stellar contamination and instrument-specific noise from exoplanet transmission spectra using neural networks, in particular the so-called Denoising AutoEncoders (DAEs). Our goals are to enable fast, accurate corrections that improve the reliability of atmospheric parameter retrievals and to promote the use of unsupervised algorithms for efficient data processing. We designed and trained DAE architectures using large synthetic datasets of terrestrial (TRAPPIST-1e analogues) and sub-Neptune (K2-18b analogues) planets. Atmospheric retrieval experiments were then performed on contaminated spectra in order to compare our deep-learning approach against standard correction methods in terms of accuracy and computational cost. Our autoencoders successfully reconstruct uncontaminated spectra, preserving essential molecular features even in low-S/N regimes. In retrieval tests, the denoising autoencoder pre-processing yields atmospheric parameter estimates broadly comparable to those obtained with simultaneous stellar-contamination fitting. Notably, our method maintains a much lower computational cost, approximately one order of magnitude smaller. These results demonstrate that DAEs outperform conventional correction methods in computational efficiency while maintaining high accuracy, paving the way for their integration into future atmospheric characterization pipelines for both rocky and sub-Neptune exoplanets.

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