The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use molecular templates to isolate a planet's spectrum from its host star. However, reliance on signal-to-noise ratio (S/N) metrics can lead to missed discoveries, due to strong assumptions of Gaussian independent and identically distributed noise. We introduce machine learning for cross-correlation spectroscopy (MLCCS); the method aims to leverage weak assumptions on exoplanet characterisation, such as the presence of specific molecules in atmospheres, to improve detection sensitivity for exoplanets. MLCCS methods, including a perceptron and unidimensional convolutional neural networks, operate in the cross-correlated spectral dimension, in which patterns from molecules can be identified. We test on mock datasets of synthetic planets inserted into real noise from SINFONI at K-band. The results from MLCCS show outstanding improvements. The outcome on a grid of faint synthetic gas giants shows that for a false discovery rate up to 5%, a perceptron can detect about 26 times the amount of planets compared to an S/N metric. This factor increases up to 77 times with convolutional neural networks, with a statistical sensitivity shift from 0.7% to 55.5%. In addition, MLCCS methods show a drastic improvement in detection confidence and conspicuity on imaging spectroscopy. Once trained, MLCCS methods offer sensitive and rapid detection of exoplanets and their molecular species in the spectral dimension. They handle systematic noise and challenging seeing conditions, can adapt to many spectroscopic instruments and modes, and are versatile regarding atmospheric characteristics, which can enable identification of various planets in archival and future data.

翻译：新一代天文台和仪器（VLT/ERIS、JWST、ELT）的发展推动了对鲁棒方法的需求，以探测和表征微弱且近距离的系外行星。光谱学中的分子映射与交叉相关技术利用分子模板从宿主恒星光谱中分离出行星光谱。然而，由于对高斯独立同分布噪声的强假设，依赖信噪比（S/N）指标可能导致遗漏发现。我们引入了用于交叉相关光谱学的机器学习方法（MLCCS）；该方法旨在利用对系外行星特征的弱假设（如大气中特定分子的存在）来提高系外行星的探测灵敏度。MLCCS方法（包括感知器和一维卷积神经网络）在交叉相关光谱维度上运行，可识别其中的分子模式。我们在K波段SINFONI仪器真实噪声中插入合成行星的模拟数据集上进行了测试。MLCCS的结果显示出显著的改进。对一组微弱合成气态巨行星的测试结果表明，在误报率不超过5%的条件下，感知器可探测到的行星数量约为S/N指标的26倍。使用卷积神经网络时，该因子可增至77倍，统计灵敏度从0.7%提升至55.5%。此外，MLCCS方法在成像光谱学中的探测置信度和显著性也表现出显著提升。一旦训练完成，MLCCS方法可在光谱维度上实现灵敏、快速的系外行星及其分子种类探测。该方法能够处理系统噪声和具有挑战性的视宁度条件，可适配多种光谱仪器和观测模式，并对大气特征具有普适性，从而能够在存档数据与未来数据中识别各类行星。