Related papers: Machine Learning the Dark Matter Halo Mass of Milk…
Although numerous dynamical techniques have been developed to estimate the total dark matter halo mass of the Milky Way, it remains poorly constrained, with typical systematic uncertainties of 0.3 dex. In this study, we apply a neural…
We show that the mass of a dark matter halo can be inferred from the dynamical status of its satellite galaxies. Using 9 dark-matter simulations of halos like the Milky Way (MW), we find that the present-day substructures in each halo…
As the Milky Way and its satellite system become more entrenched in near field cosmology efforts, the need for an accurate mass estimate of the Milky Way's dark matter halo is increasingly critical. With the second and early third data…
The mass of the dark matter halo of the Milky Way can be estimated by fitting analytical models to the phase-space distribution of dynamical tracers. We test this approach using realistic mock stellar halos constructed from the Aquarius…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
The lack of tangible evidence for non-gravitational interactions between dark and visible sectors drives the need for exploring new avenues of inferring dark matter properties through purely gravitational probes. In particular, addressing…
The sensitivity of direct detection experiments depends on the phase-space distribution of dark matter near the Sun, which can be modeled theoretically using cosmological hydrodynamical simulations of Milky Way-like galaxies. However,…
On small scales there have been a number of claims of discrepancies between the standard Cold Dark Matter (CDM) model and observations. The 'missing satellites problem' infamously describes the overabundance of subhalos from CDM simulations…
The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more…
The distribution of shapes of galaxies' dark halos provides a basic test for models of galaxy formation. To-date, few dark halo shapes have been measured, and the results of different methods appear contradictory. Here, we add to the sample…
We develop a comprehensive and flexible model for the connection between satellite galaxies and dark matter subhalos in dark matter-only zoom-in simulations of Milky Way (MW)--mass host halos. We systematically identify the physical and…
We derive the mass model of the Milky Way (MW) using a cored dark matter (DM) halo profile and recent data. The method used consists in fitting a spherically symmetric model of the Galaxy with a Burkert DM halo profile to available data: MW…
We have conducted N-body simulations of the growth of Milky Way-sized halos in cold and warm dark matter cosmologies. The number of dark matter satellites in our simulated Milky Ways decreases with decreasing mass of the dark matter…
Studying the trajectories of objects like stars, globular clusters or satellite galaxies in the Milky Way allows to trace the dark matter halo but requires reliable models of its gravitational potential. Realistic, yet simple and fully…
We propose a versatile and accurate method to estimate the halo mass and concentration from the kinematics of satellite galaxies. We construct the 6D phase-space distribution function of satellites from a cosmological simulation based on…
We introduce a new methodology to robustly determine the mass profile, as well as the overall distribution, of Local Group satellite galaxies. Specifically we employ a statistical multilevel modelling technique, Bayesian hierarchical…
High precision proper motion (PM) measurements are available for approximately 20% of all known dwarf satellite galaxies of the Milky Way (MW). Here we extend the Bayesian framework of Patel et al. (2017b) to include all MW satellites with…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
Semi-analytic modeling furnishes an efficient avenue for characterizing the properties of dark matter halos associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance,…
I review current efforts to measure the mean density of dark matter near the Sun. This encodes valuable dynamical information about our Galaxy and is also of great importance for 'direct detection' dark matter experiments. I discuss…