Measuring Galactic dark matter through unsupervised machine learning

Buckley, Matthew R.; Lim, S.; Putney, Eric; Shih, David

doi:10.1093/mnras/stad843

Cited by 3 publications

(1 citation statement)

References 83 publications

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“…Sanderson et al (2020, hereafter S20) produced nine Gaia DR2 synthetic surveys of the Latte suite of simulations (Wetzel & Hopkins 2016;Hopkins et al 2018), using the code Ananke. Such synthetic surveys have been used in many studies involving the dynamics of the MW, for example to estimate the detectability of simulated stellar streams (Shipp et al 2023), as a training set for a neural network that built the first catalog of accreted stars in the MW (Ostdiek et al 2020), leading to the discovery of the prograde local structure Nyx (Necib et al 2020), as a framework to test the ability of unsupervised machine learning techniques to reproduce the stellar phase-space density (Buckley et al 2023), and as a link to connect the formation history and the components of the MW (Belokurov et al 2020). In this work, we present synthetic Gaia DR3 surveys based on the same suite of Latte simulations.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Gaia DR3 Surveys from the FIRE Cosmological Simulations of Milky Way-mass Galaxies

Nguyen,

Ou,

Panithanpaisal

et al. 2024

ApJ

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The third data release (DR3) of Gaia has provided a fivefold increase in the number of radial velocity measurements of stars, as well as a stark improvement in parallax and proper motion measurements. To help with studies that seek to test models and interpret Gaia DR3, we present nine Gaia synthetic surveys, based on three solar positions in three Milky Way-mass galaxies of the Latte suite of the Fire-2 cosmological simulations. These synthetic surveys match the selection function, radial velocity measurements, and photometry of Gaia DR3, adapting the code base Ananke, previously used to match the Gaia DR2 release by Sanderson et al. The synthetic surveys are publicly available and can be found at http://ananke.hub.yt/. Similarly to the previous release of Ananke, these surveys are based on cosmological simulations and thus are able to model nonequilibrium dynamical effects, making them a useful tool in testing and interpreting Gaia DR3.

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Section: Introductionmentioning

confidence: 99%

Synthetic Gaia DR3 Surveys from the FIRE Cosmological Simulations of Milky Way-mass Galaxies

Nguyen,

Ou,

Panithanpaisal

et al. 2024

ApJ

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Recovering the gravitational potential in a rotating frame: Deep Potential applied to a simulated barred galaxy

Kalda,

Green,

Ghosh

2023

Monthly Notices of the Royal Astronomical Society

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Stellar kinematics provides a window into the gravitational field, and therefore into the distribution of all mass, including dark matter. Deep Potential is a method for determining the gravitational potential from a snapshot of stellar positions in phase space, using mathematical tools borrowed from deep learning to model the distribution function and solve the Collisionless Boltzmann Equation. In this work, we extend the Deep Potential method to rotating systems, and then demonstrate that it can accurately recover the gravitational potential, density distribution and pattern speed of a simulated barred disc galaxy, using only a frozen snapshot of the stellar velocities. We demonstrate that we are able to recover the bar pattern speed to within 15% in our simulated galaxy using stars in a 4kpc sub-volume centered on a Solar-like position, and to within 20% in a 2kpc sub-volume. In addition, by subtracting the mock “observed” stellar density from the recovered total density, we are able to infer the radial profile of the dark matter density in our simulated galaxy. This extension of Deep Potential is an important step in allowing its application to the Milky Way, which has rotating features, such as a central bar and spiral arms, and may moreover provide a new method of determining the pattern speed of the Milky Way bar.

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GalaxyFlow: upsampling hydrodynamical simulations for realistic mock stellar catalogues

Lim,

Raman,

Buckley

et al. 2024

Monthly Notices of the Royal Astronomical Society

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Cosmological N-body simulations of galaxies operate at the level of “star particles” with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires “upsampling” the star particles into individual stars following the same phase-space density. In this paper, we introduce two new upsampling methods. First, we describe GalaxyFlow, a sophisticated upsampling method that utilizes normalizing flows to both estimate the stellar phase space density and sample from it. Second, we improve on existing upsamplers based on adaptive kernel density estimation, using maximum likelihood estimation to fine-tune the bandwidth for such algorithms in a way that improves both the density estimation accuracy and upsampling results. We demonstrate our upsampling techniques on a neighborhood of the Solar location in two simulated galaxies: Auriga 6 and h277. Both yield smooth stellar distributions that closely resemble the stellar densities seen in the Gaia DR3 catalog. Furthermore, we introduce a novel multi-model classifier test to compare the accuracy of different upsampling methods quantitatively. This test confirms that GalaxyFlow more accurately estimates the density of the underlying star particles than methods based on kernel density estimation, at the cost of being more computationally intensive.

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Measuring Galactic dark matter through unsupervised machine learning

Cited by 3 publications

References 83 publications

Synthetic Gaia DR3 Surveys from the FIRE Cosmological Simulations of Milky Way-mass Galaxies

Synthetic Gaia DR3 Surveys from the FIRE Cosmological Simulations of Milky Way-mass Galaxies

Recovering the gravitational potential in a rotating frame: Deep Potential applied to a simulated barred galaxy

GalaxyFlow: upsampling hydrodynamical simulations for realistic mock stellar catalogues

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