A sensitivity analysis of interstellar ice chemistry in astrochemical models

Astrochemical models are essential to bridging the gap between the timescales of reactions, experiments, and observations. The ice chemistry in these models is faced with a significant computational complexity given the many parameters required to modeling the chemical processes occurring on these ices, such as binding energies and reaction energy barriers. Many of these parameters are poorly constrained and attempting to accurately determine all of them would be too costly. We aim to find out which specific parameters related to the ice chemistry have a large effect on the calculated abundances of ices for different prestellar objects. Using Monte Carlo sampled binding energies, diffusion barriers, desorption and diffusion prefactors, and reaction energy barriers, we determined the sensitivity of the abundances of the main ice species calculated with the UCLCHEM astrochemical modeling code on each of these parameters. We did this for a large grid of physical conditions across temperature, density, cosmic ray ionization rate, and UV field strength. Regardless of the physical conditions, the main sensitivities for the abundances of the main ice species are the diffusion barriers of small and relatively mobile reactive species such as , , , , and . Therefore, these parameters should be determined more accurately to increase the accuracy of models, paving the way to a better understanding of observations of interstellar ices. In many cases, accurate reaction energy barriers are not essential due to the competition between reactions and diffusion. H N O HCO CH3

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-12-21
DOI
https://doi.org/10.1051/0004-6361/202557925/pdf
Primary Topic
Astrophysics and Star Formation Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A sensitivity analysis of interstellar ice chemistry in astrochemical models

Thanja Lamberts, Tobias M. Dijkhuis, Herma M. Cuppen, Serena Viti
Springer Link (Chiba Institute of Technology)
Astrophysics and Star Formation Studies
article

A sensitivity analysis of interstellar ice chemistry in astrochemical models

Thanja Lamberts, Tobias M. Dijkhuis, Herma M. Cuppen, Serena Viti
article en

Abstract

Astrochemical models are essential to bridging the gap between the timescales of reactions, experiments, and observations. The ice chemistry in these models is faced with a significant computational complexity given the many parameters required to modeling the chemical processes occurring on these ices, such as binding energies and reaction energy barriers. Many of these parameters are poorly constrained and attempting to accurately determine all of them would be too costly. We aim to find out which specific parameters related to the ice chemistry have a large effect on the calculated abundances of ices for different prestellar objects. Using Monte Carlo sampled binding energies, diffusion barriers, desorption and diffusion prefactors, and reaction energy barriers, we determined the sensitivity of the abundances of the main ice species calculated with the UCLCHEM astrochemical modeling code on each of these parameters. We did this for a large grid of physical conditions across temperature, density, cosmic ray ionization rate, and UV field strength. Regardless of the physical conditions, the main sensitivities for the abundances of the main ice species are the diffusion barriers of small and relatively mobile reactive species such as , , , , and . Therefore, these parameters should be determined more accurately to increase the accuracy of models, paving the way to a better understanding of observations of interstellar ices. In many cases, accurate reaction energy barriers are not essential due to the competition between reactions and diffusion. H N O HCO CH3

Springer Link (Chiba Institute of Technology)
Affordable and clean energy
Openalex Percentile: Top 30%
Astrophysics and Star Formation Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.