Mapping the Chemical Evolution of Disk Galaxies with MaNGA

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

The chemical composition of galaxies - the relative amounts each galaxy has of hydrogen, helium, oxgen, nitrogen, iron, etc. - provides important clues to how those galaxies have evolved. However, the known drivers of galaxy evolution - acquiring gas that can form stars, the types of stars that form and where they form in the galaxy, and dynamical processes, such as the reistribution of gas within and outside galaxies through supernova explosions, etc. - are difficult to separate when the sample of galaxies is small, and all the galaxies in the sample are very similar. This proposal plans to develop the computer codes needed look at a sample of several thousand galaxies with very different properties in in order to provide insight into how their chemical compositions are related to the drivers of galaxy evolution. Explaining how astronomers are able to gather and use information about stars and gas by looking at the different wavelengths of light (their electromagnetic spectrum) is challenging. Yet these spectra lie at the root of most of our measurements of the masses, motions, temperatures, and compositions of stars, planets, nebulae, galaxies, and large scale structure in the Universe. Many difficult concepts, such as the phases of the moon, can be effectively taught with the aid of demonstrations. The PI will work with an undergraduate student to design and build a portable spectrograph - a portable version of an instrument that astronomers use to study the different wavelengths of light - to be used in classrooms and for public outreach. This proposal will develop and publish a sophisticated code to measure nebular abundance gradients in MaNGA. This will enable the study of nebular abundance gradients in disk galaxies, which offer a uniquely powerful probe of the processes that drive galaxy evolution: gas accretion, star formation, and gas outflows. The complex interplay of these processes is challenging to disentangle even in our own Milky Way. However, insights into these processes can be gained by examining galaxies in a wide variety of evolutionary states. The development of this code is a key step in being able to analyze the information from the MaNGA dataset.