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Astronomy postdoc. Works with machine learning & star clusters.

Queer 🏳️‍🌈, trans (she/her), vegan.

#Python #Astronomy #Astrodon #MachineLearning #LGBT #Tech

LocationGermany
Websitehttps://emilydoesastro.com
GitHubhttps://github.com/emilyhunt

A histogram of all cluster mass function bins (see below) has almost all compatible with expected values from a Kroupa IMF, within a little bit of scatter.

Some notable outliers (probably method issues) are discussed in the paper.

This is really significant because many other cluster mass papers find a varying IMF.

However, we show that almost all variation can be explained by not accounting for selection effects.

For the theorists out there, we also set some really cool constraints on cluster formation and destruction with this catalogue!

We derive the first ever Gaia global cluster mass function (+ confirm the Gaia age function too), showing that low-mass clusters are destroyed faster. These results set new constraints on the star cluster formation and destruction rate.

Having this many accurate cluster masses for the first time is super exciting, and one thing I noticed quickly is that the completeness of our catalogue depends strongly on mass.

We derived a ~100% completeness limit as a function of cluster mass for the catalogue!

From this completeness estimate, we show we're about ~100% complete for clusters above 100 solar masses within 1 kpc, or ~100% complete for clusters above 230 solar masses within 1.8 kpc.

Measuring masses for 6957 clusters as a part of this work was *not* trivial, and trying to do it accurately means there are a lot of insights in the paper!

I don't think any other paper has incorporated selection effect corrections in cluster masses, but it makes a BIG difference.

Naively, an old cluster like Trumpler 5 looks like it lacks low-mass stars. But after corrections, its mass function is compatible with a Kroupa IMF!

A bound cluster has some radius within which its gravity is stronger than the Milky Way (i.e., it has enough mass within some radius), whereas an unbound one won't have enough mass at any radius to be self-gravitating.

It works! It can reliably tell them apart!

To show you a couple of examples, these two new clusters are statistically significant young groups of stars - but they don't look 'clumpy' like an open cluster.

HSC 1131 looks more like a stream of stars, and HSC 2376 like an unbound OB association.

In our cluster catalogue from last year, we created the largest ever deduplicated catalogue of star clusters in the Milky Way.

But it came with a catch - we also found hundreds of new clusters near to the Sun, but many didn't look like gravitationally bound open clusters.

Our galaxy's young star clusters now have accurately measured masses, radii, and classifications!

My new paper out today uses a new method to distinguish between bound and unbound clusters, improving the usability of the cluster census and setting new observational constraints.

Coming soon on tomorrow's arXiv 👀 I am SO excited to talk about the last part of my PhD! 🔭

There's so much in it, including: a better way to define open clusters observationally, 6957 clusters with accurate masses, and new observational constraints on star cluster formation and destruction!

Ideally, I'd like to find a project/fellowship where I can keep working with Gaia data and/or star clusters.

But my masters' project was about determining galaxy redshifts with machine learning! So I'm also up for a change if you have an interesting project.