Molecular astronomy is a field that is blooming in the era of large observatories such as the Atacama Large Millimeter/Submillimeter Array (ALMA). With modern, sensitive, and high spectral resolution radio telescopes like ALMA and the Square Kilometer Array, the size of the data cubes is rapidly escalating, generating a need for powerful automatic analysis tools. This work introduces MolPred, a pilot study to perform predictions of molecular parameters such as excitation temperature (T_ex) and column density (log(N)) from input spectra by the use of neural networks. We used as test cases the spectra of CO, HCO⁺, SiO and CH₃CN between 80 and 400 GHz. Training spectra were generated with MADCUBA, a state-of-the-art spectral analysis tool. Our algorithm was designed to allow the generation of predictions for multiple molecules in parallel. Using neural networks, we can predict the column density and excitation temperature of these molecules with a mean absolute error of 8.5% for CO, 4.1% for HCO⁺, 1.5% for SiO and 1.6% for CH₃CN. The prediction accuracy depends on the noise level, line saturation, and number of transitions. We performed predictions upon real ALMA data. The values predicted by our neural network for this real data differ by 13% from the MADCUBA values on average. Current limitations of our tool include not considering linewidth, source size, multiple velocity components, and line blending.

分子天文学是在阿塔卡马大型毫米波/亚毫米波阵列 (ALMA) 等大型天文台时代蓬勃发展的领域。随着现代、灵敏和高光谱分辨率射电望远镜（如 ALMA 和平方公里阵列）的出现，数据立方体的大小正在迅速增加，从而产生对强大的自动分析工具的需求。这项工作介绍了MolPred，这是一项试点研究，通过使用神经网络从输入光谱中预测分子参数，例如激发温度 (T _ex ) 和柱密度 ( l o g ( N ))。我们使用 CO、HCO ⁺、SiO 和 CH ₃的光谱作为测试用例CN 在 80 到 400 GHz 之间。训练光谱是使用最先进的光谱分析工具 MADCUBA 生成的。我们的算法旨在允许并行生成多个分子的预测。使用神经网络，我们可以预测这些分子的柱密度和激发温度，CO 的平均绝对误差为 8.5%，HCO ⁺为 4.1 %，SiO 为 1.5%，CH ₃ CN为 1.6% 。预测精度取决于噪声水平、线饱和度和转换次数。我们对真实的 ALMA 数据进行了预测。我们的神经网络为这些真实数据预测的值与 MADCUBA 值平均相差 13%。我们工具的当前限制包括不考虑线宽、源大小、多速度分量和线混合。