arXiv subjects

Adriana Dropulic

Publications and source records attributed to Adriana Dropulic.

5 recordsLinked to original sources

arXiv2026

Baryon-Accelerated Core Collapse in SIDM Halos and its Imprint on Galactic Disks

The gravitational coupling between dark matter (DM) halos and the baryonic structures they host is one of the most powerful windows into the particle nature of DM. Self-interacting dark matter (SIDM) presents a minimal, well-motivated extension to the dark sector with dramatic consequences for the structure of galaxies and their halos. However, the impact of baryons on SIDM halo evolution and the resulting galactic structure has been underexplored in Milky Way (MW)-size galaxies. In this paper, we demonstrate that the inclusion of a baryonic component in a MW-size galaxy causes accelerated core collapse to begin within the MW's lifetime for a cross section as low as $σ/m = 1 \, \rm{cm}^2/\rm{g}$. We present a suite of $N$-body simulations of cold dark matter and SIDM MW-size galaxies with and without a baryonic component for cross sections $σ/m =[1.0, 2.5, 5.0]$ cm$^2$/g. We find numerically, and semi-analytically, that the presence of a stellar disk and bulge shortens the predicted core collapse timescales from the DM only simulations by a factor of $\sim 40$. Further, as the core collapse begins within the lifetime of the galaxy, the subsequent density increase strengthens the mid-plane restoring force exerted on stellar disk orbits, leading the disk to flare. This work quantifies both directions of the baryon--SIDM coupling: baryons accelerate core collapse in MW-sized halos, and the resulting halo evolution reshapes the disk through thinning and flaring. Both processes open new observational windows into DM.

astro-ph.GA↗

arXiv2024

Efficient and accurate force replay in cosmological-baryonic simulations

We construct time-evolving gravitational potential models for a Milky Way-mass galaxy from the FIRE-2 suite of cosmological-baryonic simulations using basis function expansions. These models capture the angular variation with spherical harmonics for the halo and azimuthal harmonics for the disk, and the radial or meridional plane variation with splines. We fit low-order expansions (4 angular/harmonic terms) to the galaxy's potential for each snapshot, spaced roughly 25 Myr apart, over the last 4 Gyr of its evolution, then extract the forces at discrete times and interpolate them between adjacent snapshots for forward orbit integration. Our method reconstructs the forces felt by simulation particles with high fidelity, with 95% of both stars and dark matter, outside of self-gravitating subhalos, exhibiting errors $\leq$4% in both the disk and the halo. Imposing symmetry on the model systematically increases these errors, particularly for disk particles, which show greater sensitivity to imposed symmetries. The majority of orbits recovered using the models exhibit positional errors $\leq$10% for 2-3 orbital periods, with higher errors for orbits that spend more time near the galactic center. Approximate integrals of motion are retrieved with high accuracy even with a larger potential sampling interval of 200 Myr. After 4 Gyr of integration, 43% and 70% of orbits have total energy and angular momentum errors within 10%, respectively. Consequently, there is higher reliability in orbital shape parameters such as pericenters and apocenters, with errors $\sim$10% even after multiple orbital periods. These techniques have diverse applications, including studying satellite disruption in cosmological contexts.

astro-ph.GA↗

arXiv2024

StreamGen: Connecting Populations of Streams and Shells to Their Host Galaxies

In this work, we study how the abundance and dynamics of populations of disrupting satellite galaxies change systematically as a function of host galaxy properties. We apply a theoretical model of the phase-mixing process to classify intact satellite galaxies, stellar stream-like and shell-like debris in ~1500 Milky Way-mass systems generated by a semi-analytic galaxy formation code, SatGen. In particular, we test the effect of host galaxy halo mass, disk mass, ratio of disk scale height to length, and stellar feedback model on disrupting satellite populations. We find that the counts of tidal debris are consistent across all host galaxy models, within a given host mass range, and that all models can have stream-like debris on low-energy orbits, consistent with those observed around the Milky Way. However, we find a preference for stream-like debris on lower-energy orbits in models with a thicker (lower-density) host disk or on higher-energy orbits in models with a more-massive host disk. Importantly, we observe significant halo-to-halo variance across all models. These results highlight the importance of simulating and observing large samples of Milky Way-mass galaxies and accounting for variations in host properties when using disrupting satellites in studies of near-field cosmology.

astro-ph.GA↗

arXiv2022

Revealing the Milky Way's Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities

Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ~92 million stars. The network, which takes as input a star's parallax, angular coordinates, and proper motions, is trained and validated on ~6.4 million stars in Gaia with complete phase-space information. The network's uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalog to identify candidate stars that belong to the Milky Way's most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ~450,000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network's predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate and apply such a neural network when complete observational data is not available.

astro-ph.GA↗

arXiv2021

Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations

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 ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ~5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.

astro-ph.GA↗