A Fast Machine Learning Tool To Predict The Composition Of Astronomical Ices From Infrared Absorption Spectra - Astrobiology
作者：Keith Cowing
Performance of our neural networks after the training with the first training+validation split, with respect to the spectra included in the validation subset. The plots show the predicted versus true labeled values for the molecular fractions and temperature. Left panels: Molecular fractions of ice spectra. Each ice spectrum in the validation subset (with a certain composition and temperature) corresponds to six circles in the plot: one for each targeted molecule (denoted by colours). The sizes of the circles indicate the temperature of the ice. The bottom panel reports the difference between the predicted molecular fraction and the labeled value from the experiment. Right panels: Temperature of ice spectra, represented by each circle. Again, the bottom panel reports the difference between the predicted and the labeled values.– astro-ph.IM
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 H2O, CO, and CO2, 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 H2O, CO, CO2, CH3OH, NH3, and CH4. 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 ∼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.
Andrés Megías, Izaskun Jiménez-Serra, François Dulieu, Julie Vitorino, Belén Maté, David Ciudad, Will R. M. Rocha, Marcos Martínez Jiménez, Jacobo Aguirre
Comments: 24 pages, 20 figures; accepted to be published in Astronomy & Astrophysics
Subjects: Astrophysics of Galaxies (astro-ph.GA); Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Solar and Stellar Astrophysics (astro-ph.SR)
Cite as: arXiv:2509.04331 [astro-ph.GA] (or arXiv:2509.04331v1 [astro-ph.GA] for this version)
https://doi.org/10.48550/arXiv.2509.04331
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Submission history
From: Andrés Megías
[v1] Thu, 4 Sep 2025 15:49:28 UTC (6,652 KB)
https://arxiv.org/abs/2509.04331
Astrobiology, Astrochemistry,
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