Mapping of Ice and Gas on 1000 AU Scales

Suutarinen, Aleksi Niko (2015). Mapping of Ice and Gas on 1000 AU Scales. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.0000ef7d

Abstract

Many of the molecules in interstellar space are in the solid state, in so-called interstellar ices. The research in this thesis is focused around two key astronomical questions; How is ice distributed in star forming regions? How is ice affected by - or affecting - star formation processes? I provide answers to these questions through the analysis of submillimeter and near-infrared observations.

The observations analysed in this thesis consist of (partially published) archival data acquired mainly with the AKARI and Herschel space telescopes, and the ground-based ESO/VLT. To facilitate the reduction and analysis of some of this data two major software packages (ARF2 and Omnifit) were created with the Python programming language The operation of both packages is fully documented in the thesis appendix.

The study of methanol ice prevalence in star-forming regions found that methanol ice can be found towards many more lines of sight than previously reported, and that its abundance relative to water ice can vary between a few to ~40%. I also confirm that methanol very likely exists mixed in a water-rich ice component, a result consistent with our current understanding of methanol ice formation.

Proof was found of high-temperature chemistry forming water in the warm postshock gas of YSOs. In this same region it was found that up to 99% of the methanol is being destroyed as it is sputtered from the surfaces of dust grains into the gas phase.

A novel analysis technique of slitless AKARI near-infrared spectroscopy yields an unprecedented number of water ice column density estimates towards background star lines of sight covering 12 separate 10' x 10' fields of view in as many molecular clouds. A moderate correlation is found between water ice column density and dust optical depth at 250 microns, with the correlation potentially varying from cloud to cloud.

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