用机器学习预测系外行星生物信号通量，助力太空望远镜高效选目标。
Life, Machine Learning, and the Search for Habitability: Predicting Biosignature Fluxes for the Habitable Worlds Observatory
- 设计两种新模型：贝叶斯卷积网络与光谱查询自适应变压器。
- 在多种系外行星条件下预测准确率高，且能量化不确定性。
- 适合天体物理与人工智能交叉研究者，助力建设未来空间望远镜观测策略。
未来直接成像旗舰任务（如美国宇航局的宜居世界观测台，HWO）面临观测时间与资源极度紧张的挑战。本文提出两种面向系外行星反射光谱中生物信号物种通量预测的先进机器学习架构：贝叶斯卷积神经网络（BCNN）和新型模型光谱查询自适应变压器（SQuAT）。BCNN可同时量化认知不确定性与随机不确定性，在多变观测条件下提供可靠预测；而SQuAT采用查询驱动注意力机制，显著提升光谱特征与特定生物信号物种之间的可解释性关联。我们在扩展数据集上验证了两模型在广泛系外行星条件下的高预测精度，凸显其在不确定性量化与光谱可解释性上的独特优势。这些能力使其成为加速目标筛选、优化观测计划、最大化科学回报的关键工具，适用于下一代旗舰任务如HWO。
原文摘要 · Abstract (English)
Future direct-imaging flagship missions, such as NASA's Habitable Worlds Observatory (HWO), face critical decisions in prioritizing observations due to extremely stringent time and resource constraints. In this paper, we introduce two advanced machine-learning architectures tailored for predicting biosignature species fluxes from exoplanetary reflected-light spectra: a Bayesian Convolutional Neural Network (BCNN) and our novel model architecture, the Spectral Query Adaptive Transformer (SQuAT). The BCNN robustly quantifies both epistemic and aleatoric uncertainties, offering reliable predictions under diverse observational conditions, whereas SQuAT employs query-driven attention mechanisms to enhance interpretability by explicitly associating spectral features with specific biosignature species. We demonstrate that both models achieve comparably high predictive accuracy on an augmented dataset spanning a wide range of exoplanetary conditions, while highlighting their distinct advantages in uncertainty quantification and spectral interpretability. These capabilities position our methods as promising tools for accelerating target triage, optimizing observation schedules, and maximizing scientific return for upcoming flagship missions such as HWO.
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