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Tomography of the Milky waY

Periodic Reporting for period 1 - TOMMY (Tomography of the Milky waY)

Reporting period: 2018-11-01 to 2020-10-31

Our understanding of the Milky Way is undergoing a revolution. During the project Europe's Gaia satellite released its first exquisite measurements, tracking more than a billion stars. In conjunction with ground-based telescopes, Gaia data has the power to produce results that stand the test of time; results that will give us a fundamental understanding of our home Galaxy and its place in the universe. The project's objectives focused on using this data to improve our understanding of some of the most important ingredients of the Milky Way:

- Understanding how the Milky Way's stars are distributed.
- Understanding how the elements which are locked into those stars are distributed.
- Understanding how the Galaxy's dark matter is distributed.

All of these are key to helping us to understand how the Milky Way formed and evolved into the Galaxy we live in today.
The objectives were addressed through three different approaches.

By taking spectra of stars we could measure their velocities and the amount of different elements each star contains using the characteristic features produced when each element absorbs starlight. We took spectra of stars located in the Milky Way's bar using the European VLT telescope in Chile. When combined with measurements from the Gaia satellite, we could see how each star was moving in three dimensions and, by comparing to models, we could see which of these stars were part of the Milky Way's bar. Interestingly, we found that the stars in the bar contained large quantities of iron. Because iron-rich stars formed more recently than their iron-poor counterparts, this is telling us how the bar formed and evolved. It could be that these younger stars in the bar formed in the bar, or it could be that the Milky Way's bar is growing, and capturing young stars as it does. Both scenarios are possible, and we hope that better data over the coming few years from a new generation of large surveys will tell us which narrative is correct.

To understand how the dark matter is distributed we used a rare and special type of star called RR Lyrae. The Gaia satellite has measured proper motions (meaning the motion across the sky) for more than a billion stars across the Galaxy. However, unfortunately for most of these stars we do not have the distances we need to turn these proper motions into real velocities. RR Lyrae are a type of pulsating star where we can estimate accurate distances. We therefore used a sample of almost 16,000 RR Lyrae measured by Gaia to measure the velocities of the stars which make up the Milky Way's stellar halo. We expect our galaxy to be evolving slowly, on a timescale of billions of years, and so we used these velocities to tell us about the forces holding our Galaxy together. For example if the velocities of the stars are high, then the gravitational forces must be strong, otherwise the Milky Way would not be held together and would fly apart in a relatively short timescale. We were therefore able to measure the gravitational forces across the Milky Way using this sample, and we could even do so in two-dimensions giving us a direction to forces at each point in space (see attached plot). The forces we found are too strong to be produced by the stars and matter that we can see, with the remainder produced by unseen dark matter. Interestingly, because the forces point nearly towards the galactic center, this means that the dark matter that surrounds us must be mostly spherical in shape.

The project also developed tools that will allow others to build on this work. A web-based tool that lets anyone make a mock survey of the Milky Way, taking fake measurements of a model Milky Way, will allow others both to design fruitful new surveys, as well as help to understand already taken data. Furthermore, a software package was developed that uses machine learning techniques that are normally used to train neural networks to categorize images to instead make models of the Milky Way. The model of the Milky Way is analogous to the neural network - it is trained until it can correctly predict the data that we take on the Milky Way. This package has been made open source to allow others to use and improve on its methods.
The project has shed light on the makeup of the bar and the inner Galaxy, and provided some of the first indications on how it may have evolved. This is partly important because the Milky Way is our home Galaxy, but also because we expect that other similar galaxies will have evolved similarly. Therefore these results from the project likely indicate how all barred galaxies evolved. Because of the success of this element of the project we have instigated a follow up campaign to measure more stars using the VLT telescope in Chile.

We measured the gravitational force field across the halo of the Milky Way for the first time (see attached figure) a strikingly visual result. By going beyond the state of the art here, we were able to for the first time measure how the shape of the dark matter in the Milky Way changes as we move from the center out. We found that it is remarkably spherical at all radii, something that isn't expected in most simulations of dark matter made so far, and may hint at its properties. This is important because dark matter makes up the majority of the matter in the universe, but we know remarkably little about how it may interact.
Understanding the Milky Way is hampered by dust. This is a dust free view of the Milky Way.
The gravitational force field of the Milky Way measured during the project.