Automatic Ice Composition Estimator (AICE) Archives

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

Astrochemistry, Astrogeology, Astronomy & Telescopes, Biochemistry & Organic Chemistry, Biosignatures & Paleobiology, Imaging & Spectroscopy, Interstellar, Status Report

A Fast Machine Learning Tool To Predict The Composition Of Astronomical Ices From Infrared Absorption Spectra

by Keith CowingSeptember 5, 2025September 5, 2025

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, […]

Automatic Ice Composition Estimator (AICE)

A Fast Machine Learning Tool To Predict The Composition Of Astronomical Ices From Infrared Absorption Spectra

Dragonfly Titan Astrobiology Droid Clears Key Tests

Expedition Led By MBARI Researchers Reveals A Remarkable, And Rare, Form Of Deep-sea Hydrothermal Vents

SwRI Study Found Primordial Mini-moons May Explain Meteorite Composition

The Galactic Centre G+0.633-0.0604 Molecular Cloud: A New Astrochemical Gold Mine. I. Gas Physical Properties

Researchers Confirm The Cause Of Earth’s Biggest Mass Extinction