Related papers: Sensitivity Estimation for Dark Matter Subhalos in…
Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide valuable information about the nature of DM. Stellar wakes, induced by…
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 goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.…
Despite the Milky Way's proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being…
We present a wavelet-based algorithm to identify dwarf galaxies in the Milky Way in ${\it Gaia}$ DR2 data. Our algorithm detects overdensities in 4D position--proper motion space, making it the first search to explicitly use velocity…
The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the…
High-resolution N-body simulations of dark matter halos indicate that the Milky Way contains numerous subhalos. When a dark matter subhalo passes in front of a star, the light from that star will be deflected by gravitational lensing,…
The detection of dark matter subhalos without a stellar component in the Galactic halo remains a challenge. We use supervised machine learning to identify high-latitude gamma-ray sources with dark matter-like spectra among unassociated…
Stellar streams are a promising way to probe the gravitational effects of low-mass dark matter (DM) subhalos. In recent years, there has been a remarkable explosion in the number of stellar streams detected in the Milky Way, and hundreds…
Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved…
Small-scale dark matter structures lighter than a billion solar masses are an important probe of primordial density fluctuations and dark matter microphysics. Due to their lack of starlight emission, their only guaranteed signatures are…
We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase space distribution of…
The structure, extent, and mass of the Milky Way's (MW) dark matter (DM) halo are observationally challenging to determine due to our position within the Galaxy. To overcome this limitation, we study a combined sample of 127 MW analogs from…
We present quantitative predictions for the detectability of individual Galactic dark matter subhalos in gamma-rays from dark matter pair annihilations in their centers. Our method is based on a hybrid approach, employing the highest…
We use a catalogue of stellar binaries with wide separations (up to 1 pc) identified by the Gaia satellite to constrain the presence of extended substructure within the Milky Way galaxy. Heating of the binaries through repeated encounters…
The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the…
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 observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
We present a novel, data-driven analysis of Galactic dynamics, using unsupervised machine learning -- in the form of density estimation with normalizing flows -- to learn the underlying phase space distribution of 6 million nearby stars…
We present a method to identify Ultra Faint Dwarf Galaxy (UFDG) candidates in the halo of the Milky Way using the future Gaia catalogue and we explore its detection limits and completeness. The method is based on the Wavelet Transform and…