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Register a new userSearching for Dwarf Galaxies in ${it Gaia}$ DR2 Phase-Space Data Using Wavelet Transforms
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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 information to search for dwarf galaxy candidates. We optimize our algorithm and quantify its performance by searching for mock dwarfs injected into ${it Gaia}$ DR2 data and for known Milky Way satellite galaxies. Comparing our results with previous photometric searches, we find that our search is sensitive to undiscovered systems at Galactic latitudes~$lvert brvert>20^{circ}$ and with half-light radii larger than the 50% detection efficiency threshold for Pan-STARRS1 (PS1) at (${it i}$) absolute magnitudes of =$-7<M_V<-3$ and distances of $32$ kpc $< D < 64$ kpc, and (${it ii}$) $M_V< -4$ and $64$ kpc $< D < 128$ kpc. Based on these results, we predict that our search is expected to discover $5 pm 2$ new satellite galaxies: four in the PS1 footprint and one outside the Dark Energy Survey and PS1 footprints. We apply our algorithm to the ${it Gaia}$ DR2 dataset and recover $sim 830$ high-significance candidates, out of which we identify a gold standard list of $sim 200$ candidates based on cross-matching with potential candidates identified in a preliminary search using ${it Gaia}$ EDR3 data. All of our candidate lists are publicly distributed for future follow-up studies. We show that improvements in astrometric measurements provided by ${it Gaia}$ EDR3 increase the sensitivity of this technique; we plan to continue to refine our candidate list using future data releases.
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Many models of physics beyond the Standard Model include towers of particles whose masses follow an approximately periodic pattern with little spacing between them. These resonances might be too weak to detect individually, but could be discovered as a group by looking for periodic signals in kinematic distributions. The continuous wavelet transform, which indicates how much a given frequency is present in a signal at a given time, is an ideal tool for this. In this paper, we present a series of methods through which continuous wavelet transforms can be used to discover periodic signals in kinematic distributions. Some of these methods are based on a simple test statistic, while others make use of machine learning techniques. Some of the methods are meant to be used with a particular model in mind, while others are model-independent. We find that continuous wavelet transforms can give bounds comparable to current searches and, in some cases, be sensitive to signals that would go undetected by standard experimental strategies.
We search for the fastest stars in the subset of stars with radial velocity measurements of the second data release (DR2) of the European Space Agency mission Gaia. Starting from the observed positions, parallaxes, proper motions, and radial velocities, we construct the distance and total velocity distribution of more than $7$ million stars in our Milky Way, deriving the full 6D phase space information in Galactocentric coordinates. These information are shared in a catalogue, publicly available at http://home.strw.leidenuniv.nl/~marchetti/research.html. To search for unbound stars, we then focus on stars with a probability greater than $50 %$ of being unbound from the Milky Way. This cut results in a clean sample of $125$ sources with reliable astrometric parameters and radial velocities. Of these, $20$ stars have probabilities greater than 80 $%$ of being unbound from the Galaxy. On this latter sub-sample, we perform orbit integration to characterize the stars orbital parameter distributions. As expected given the relatively small sample size of bright stars, we find no hypervelocity star candidates, stars that are moving on orbits consistent with coming from the Galactic Centre. Instead, we find $7$ hyper-runaway star candidates, coming from the Galactic disk. Surprisingly, the remaining $13$ unbound stars cannot be traced back to the Galaxy, including two of the fastest stars (around $700$ km/s). If conformed, these may constitute the tip of the iceberg of a large extragalactic population or the extreme velocity tail of stellar streams.
Using a single N-body simulation ($N=0.14times 10^9$) we explore the formation, evolution and spatial variation of the phase-space spirals similar to those recently discovered by Antoja et al. in the Milky Way disk, with Gaia DR2. For the first time in the literature, we use a self-consistent N-body simulation of an isolated Milky Way-type galaxy to show that the phase-space spirals develop naturally from vertical oscillations driven by the buckling of the stellar bar. We claim that the physical mechanism standing behind the observed incomplete phase-space mixing process can be internal and not necessarily due to the perturbation induced by a massive satellite. In our model, the bending oscillations propagate outwards and produce axisymmetric variations of the mean vertical coordinate and of the vertical velocity component. As a consequence, the phase-space wrapping results in the formation of patterns with various morphology across the disk, depending on the bar orientation, distance to the galactic center and time elapsed since the bar buckling. Once bending waves appear, they are supported for a long time via disk self-gravity. The underlying physical mechanism implies the link between in-plane and vertical motion that leads directly to phase-space structures whose amplitude and shape are in remarkable agreement with those of the phase-space spirals observed in the Milky Way disk. In our isolated galaxy simulation, phase-space spirals are still distinguishable, at the solar neighbourhood, 3 Gyr after the buckling phase. The long-lived character of the phase-space spirals generated by the bar buckling instability cast doubts on the timing argument used so far to get back at the time of the onset of the perturbation: phase-space spirals may have been caused by perturbations originated several Gyrs ago, and not as recent as suggested so far.
We make use of APOGEE and $Gaia$ data to identify stars that are consistent with being born in the same association or star cluster as the Sun. We limit our analysis to stars that match solar abundances within their uncertainties, as they could have formed from the same Giant Molecular Cloud (GMC) as the Sun. We constrain the range of orbital actions that solar siblings can have with a suite of simulations of solar birth clusters evolved in static and time-dependent tidal fields. The static components of each galaxy model are the bulge, disk, and halo, while the various time-dependent components include a bar, spiral arms, and GMCs. In galaxy models without GMCs, simulated solar siblings all have $J_R < 122$ km $rm s^{-1}$ kpc, $990 < L_z < 1986$ km $rm s^{-1}$ kpc, and $0.15 < J_z < 0.58$ km $rm s^{-1}$ kpc. Given the actions of stars in APOGEE and $Gaia$, we find 104 stars that fall within this range. One candidate in particular, Solar Sibling 1, has both chemistry and actions similar enough to the solar values that strong interactions with the bar or spiral arms are not required for it to be dynamically associated with the Sun. Adding GMCs to the potential can eject solar siblings out of the plane of the disk and increase their $J_z$, resulting in a final candidate list of 296 stars. The entire suite of simulations indicate that solar siblings should have $J_R < 122$ km $rm s^{-1}$ kpc, $353 < L_z < 2110$ km $rm s^{-1}$ kpc, and $J_z < 0.8$ km $rm s^{-1}$ kpc. Given these criteria, it is most likely that the association or cluster that the Sun was born in has reached dissolution and is not the commonly cited open cluster M67.
The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison between theory and observation. We show that the Wavelet Scattering Transform (WST), in combination with linear discriminant analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps. WST-LDA classifies magnetohydrodynamic (MHD) turbulence simulations with up to a 97% true positive rate in our testbed of 8 simulations with varying sonic and Alfv{e}nic Mach numbers. We present a side-by-side comparison with two other methods for non-Gaussian characterization, the Reduced Wavelet Scattering Transform (RWST) and the 3-Point Correlation Function (3PCF). We also demonstrate the 3D-WST-LDA and apply it to classification of density fields in position-position-velocity (PPV) space, where density correlations can be studied using velocity coherence as a proxy. WST-LDA is robust to common observational artifacts, such as striping and missing data, while also sensitive enough to extract the net magnetic field direction for sub-Alfv{e}nic turbulent density fields. We include a brief analysis of the effect of point spread functions and image pixelization on 2D-WST-LDA applied to density fields, which informs the future goal of applying WST-LDA to 2D or 3D all-sky dust maps to extract hydrodynamic parameters of interest.
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