METALS 2023 Conference

Attendees and organisers of the Metals 2023 conference at ESO, Vitacura in Santiago Chile
(photo credit to Metals 2023 organisers at ESO)

Last week I attended the metals 2023 meeting, hosted by ESO Vitacura, Santiago.

The conference brought together researchers working on Metal distribution in the Universe with a large focus on chemical abundances produced during various astrophysical events and the hierarchical nature of this chemical evolution.

For example, different types of supernovae produce different chemical elements (to name a few: SNIa produce most of the Fe, Mn, Ni…, CSSNe produce most of the O, Mg.. in the Universe).

These elements can be used to explore the chemical evolutionary history of galaxies and to trace the origin of stars (much like DNA in biological systems) as these chemical abundances change gradually over time.

As a galaxy evolves stars retain the chemical signature of their birth environment, essentially becoming fossils.

The idea is that this abundance data can then be used to trace stars back to their stellar birth environment, even when the stars themselves have travelled far from where they first formed.

I attended to present our work on applying the neighbour-joining algorithm to galactic chemical evolution (paper coming soon!).

Here I am about to entertain astronomers with the NJ algorithm – Image credit to Brian Tapia.

It was a really nice conference, I met some very cool people and had some interesting discussions about how we are using trees in the ERIS project.

There were many awesome talks, the hottest topic of the conference was – for the most part – dust!

Dust grains in the universe play an important role in star and planet formation.

Dust is also created during supernovas and it is thought that [Zn/Fe] abundances can be used as a tracer of dust in a system as Zn and Fe have different refractory properties.

It turns out that we should incorporate dust for more accurate simulations – so a better understanding of dust is of great importance for inferring how chemical abundances evolve through time.