2021 Project Description

Comparing laboratory-measured material opacity to interstellar dust models

Supervisors

Lapo Fanciullo, Sascha Zeegers
Find out more about supervisors on ASIAA website

Task Description and Goals

Interstellar dust is a collection of (mostly) sub-micron grains made primarily of silicates and carbon. Since dust is well mixed with gas, it has in important role as a tracer: observing the dust spatial distribution and mass in astronomical objects (such as interstellar clouds and galaxies), we can recover the distribution and amount of gas, which is essential to test theories of star formation and evolution.

One of the most used methods of recovering dust masses is to observe thermal dust emission at multiple wavelengths in the far-infrared and submillimeter ranges (i.e. 100 to 1000 micron) and fit the observations with a dust model. However, this method has an important limitation: the results depend on the value assumed in the model for far-infrared dust opacity. Traditionally, dust models have used values for far-infrared opacity extrapolated from shorter wavelengths assuming a simple power-law behavior. However, recent experimental results on dust analogues (silicates and carbon) have shown that their opacity is different from what previously assumed: experimental opacity is higher, it depends on temperature and does not follow a simple power-law.

Our team is working to understand how this new data may change the value of dust masses that have been estimated in the past years. We create simple models of dusty galaxies and their multi-wavelength emission using laboratory-derived opacity, then fit them with standard dust models to check whether the fit can correctly recover the galaxy? properties. We use data from laboratory opacity databases such as DOCCD (hosted by the Jena university) and SSHADE.

The student will:

• Download experimental dust opacities from online databases and prepare them (e.g. by interpolating over missing data sections);
• Create a grid of galaxy models with different dust compositions and temperature distributions, using -- and if necessary modifying -- an already-developed code.
• Fit the synthetic dust emission from the aforementioned models using -- and if necessary modifying -- an already-developed code.

Required Background

• Capable of English language communication
• Possessing basic knowledge of physics and astronomy
• Familiar with coding (preferably Python)