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arXiv 2509.04331astro-ph.GAastro-ph.EPastro-ph.IMastro-ph.SR
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一种快速的机器学习工具,用于从红外吸收光谱预测天文冰的组成

A fast machine learning tool to predict the composition of astronomical ices from infrared absorption spectra

Centro de Astrobiología (CAB), CSIC-INTA(天体生物学中心);Facultad de Ciencias Físicas, Universidad Complutense de Madrid(马德里康普顿斯大学物理科学学院);LIRA, CY Cergy Paris Université(CY塞吉巴黎大学 LIRA 实验室);Instituto de Estructura de la Materia (IEM), CSIC(物质结构研究所);MasOrange;Laboratory for Astrophysics, Leiden Observatory, Leiden University(莱顿大学莱顿天文台天体物理实验室)

Andrés Megías, Izaskun Jiménez-Serra, François Dulieu, Julie Vitorino, Belén Maté, David Ciudad, Will R. M. Rocha, Marcos Martínez Jiménez, Jacobo Aguirre

AI总结:

本文提出AICE工具,基于人工神经网络快速预测冰的组成,通过实验室数据训练,实现高效准确的冰成分分析。

摘要:

目前詹姆斯·韦伯空间望远镜(JWST)的观测使我们能够观测到覆盖星际尘粒的冰壳的吸收特征,主要由H2O、CO和CO2组成,以及其它次要物种。得益于其灵敏度和光谱分辨率,JWST有潜力观测数百个处于不同星形成阶段的源的冰特征。然而,识别不同物种的光谱特征并量化冰的组成并不容易,需要复杂的光谱分析。我们提出了自动冰组成估计器(AICE),一种基于人工神经网络的新工具。基于2.5至10微米的红外(IR)冰吸收光谱,AICE预测H2O、CO、CO2、CH3OH、NH3和CH4的冰的分数组成。为了训练模型,我们使用了数百个来自不同数据库的冰混合物实验室实验,这些实验经过基线减去和归一化处理。一旦训练完成,AICE在传统计算机上可在不到一秒的时间内预测与观测红外吸收光谱相关的冰组成,其物种分数的典型误差约为3%。我们在NIR38和J110621背景恒星的JWST Ice Age计划中测试了其性能,证明了与之前冰组成估计的一致性。AICE的快速和准确性能使系统分析数百种不同冰光谱成为可能,只需适度的时间投入。此外,该模型可以利用更多实验室数据进行增强和重新训练,提高预测精度并扩展可预测的物种列表。

英文摘要:

Current observations taken by James Webb Space Telescope (JWST) allow us to observe the absorption features of icy mantles that cover interstellar dust grains, which are mainly composed of $\mathrm{H_2O}$, $\mathrm{CO}$, and $\mathrm{CO_2}$, along with other minor species. Thanks to its sensitivity and spectral resolution, JWST has the potential to observe ice features towards hundreds of sources at different stages along the process of star formation. However, identifying the spectral features of the different species and quantifying the ice composition is not trivial and requires complex spectroscopic analysis. We present Automatic Ice Composition Estimator (AICE), a new tool based on artificial neural networks. Based on the infrared (IR) ice absorption spectrum between 2.5 and 10 microns, AICE predicts the ice fractional composition in terms of $\mathrm{H_2O}$, $\mathrm{CO}$, $\mathrm{CO_2}$, $\mathrm{CH_3OH}$, $\mathrm{NH_3}$, and $\mathrm{CH_4}$. To train the model, we used hundreds of laboratory experiments of ice mixtures from different databases, which were reprocessed with baseline subtraction and normalisation. Once trained, AICE takes less than one second on a conventional computer to predict the ice composition associated with the observed IR absorption spectrum, with typical errors of $\sim$3 $\%$ in the species fraction. We tested its performance on two spectra reported towards the NIR38 and J110621 background stars observed within the JWST Ice Age program, demonstrating a good agreement with previous estimations of the ice composition. The fast and accurate performance of AICE enables the systematic analysis of hundreds of different ice spectra with a modest time investment. In addition, this model can be enhanced and re-trained with more laboratory data, improving the precision of the predictions and expanding the list of predicted species.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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