Project D
Fireworks around new-born stars: key to pre-biotic chemistry
Marta De Simone, Łukasz Tychoniec & Alice Somigliana
Outflows from new-born stars contain complex chemical species, which allow us to constrain the physical conditions of the gas surrounding the young stars. In this project, you will use ALMA observations of one such protostar to investigate the chemical and dynamical history of the system.
As young stars are born from the clouds of dust and gas, they release a lot of the material in spectacular protostellar outflows. Those outflows have usually quite peculiar chemical composition as the launched material shocks against the surrounding envelope releasing into the gas-phase molecular species that were retained in the icy interstellar grains. One example of incredibly rich molecular outflow is S68N – a Class 0 protostar, less 100 000 years old. In the released gas we can find complex species such as methanol (CH3OH), formaldehyde (H2CO), and methyl cyanide (CH3CN). While methanol and formaldehyde are likely formed on the grain surface and released into the gas-phase after the shock passage, the formation of methyl cyanide is still poorly understood.
On the one hand, using the spectral properties of these species, it will be possible to derive the physical condition of the gas. On the other end, comparing the spatial distribution of the three species, the formation pathway of CH3CN can be constrained. All these information will be crucial to assess the chemical and dynamical history of the system.
In this project, the student will use observations from the Atacama Large Millimeter/submillimeter Array (ALMA) that cover several molecular transitions in the outflow of S68N at high spatial resolution. The student will gain expertise in standard tools of observational astrochemistry: the data will be visualized using the CASA and CARTA packages so to get familiar with most common radio interferometry tools, and the spectra will be analysed using the fitting procedures in the CASSIS/XCLASS software to derive chemical abundances. With custom-made Python scripts the student will then create images out of the ALMA data and extract quantitative information about the system.