Charting Galactic Accelerations with Stellar Streams and Machine Learning

We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, since a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a ground-truth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, though standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike, and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.Comment: 32 pages, 10 figures, Submitted for publication. Comments welcome. Code will be made available upon publicatio

Stream Members Only: Data-Driven Characterization of Stellar Streams with Mixture Density Networks

Stellar streams are sensitive probes of the Milky Way's gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise characterization of streams from potentially noisy data marks a crucial step in inferring galactic structure, including the dark matter, across orders of magnitude in mass scales. Here we present a new method for constructing a smooth probability density model of stellar streams using all of the available astrometric and photometric data. To characterize a stream's morphology and kinematics, we utilize mixture density networks to represent its on-sky track, width, stellar number density, and kinematic distribution. We model the photometry for each stream as a single-stellar population, with a distance track that is simultaneously estimated from the stream's inferred distance modulus (using photometry) and parallax distribution (using astrometry). We use normalizing flows to characterize the distribution of background stars. We apply the method to the stream GD-1, and the tidal tails of Palomar 5. For both streams we obtain a catalog of stellar membership probabilities that are made publicly available. Importantly, our model is capable of handling data with incomplete phase-space observations, making our method applicable to the growing census of Milky Way stellar streams. When applied to a population of streams, the resulting membership probabilities from our model form the required input to infer the Milky Way's dark matter distribution from the scale of the stellar halo down to subhalos.Comment: 35 pages, 13 figures, 4 tables, fully open-source and reproducible using ShowYourWor

The Simons Observatory: Galactic Science Goals and Forecasts

Observing in six frequency bands from 27 to 280 GHz over a large sky area, the Simons Observatory (SO) is poised to address many questions in Galactic astrophysics in addition to its principal cosmological goals. In this work, we provide quantitative forecasts on astrophysical parameters of interest for a range of Galactic science cases. We find that SO can: constrain the frequency spectrum of polarized dust emission at a level of $\Delta\beta_d \lesssim 0.01$ and thus test models of dust composition that predict that $\beta_d$ in polarization differs from that measured in total intensity; measure the correlation coefficient between polarized dust and synchrotron emission with a factor of two greater precision than current constraints; exclude the non-existence of exo-Oort clouds at roughly 2.9$\sigma$ if the true fraction is similar to the detection rate of giant planets; map more than 850 molecular clouds with at least 50 independent polarization measurements at 1 pc resolution; detect or place upper limits on the polarization fractions of CO(2-1) emission and anomalous microwave emission at the 0.1% level in select regions; and measure the correlation coefficient between optical starlight polarization and microwave polarized dust emission in $1^\circ$ patches for all lines of sight with $N_{\rm H} \gtrsim 2\times10^{20}$ cm$^{-2}$. The goals and forecasts outlined here provide a roadmap for other microwave polarization experiments to expand their scientific scope via Milky Way astrophysics.Comment: Submitted to AAS journals. 33 pages, 10 figure

Constraining the Gravitational Potential from the Projected Morphology of Extragalactic Tidal Streams

The positions and velocities of stellar streams have been used to constrain the mass and shape of the Milky Way's dark matter halo. Several extragalactic streams have already been detected, though it has remained unclear what can be inferred about the gravitational potential from only 2D photometric data of a stream. We present a fast method to infer halo shapes from the curvature of 2D projected stream tracks. We show that the stream curvature vector must point within 90° of the projected acceleration vector, in the absence of recent time-dependent perturbations. While insensitive to the total magnitude of the acceleration, and therefore the total mass, applying this constraint along a stream can determine halo shape parameters and place limits on disk-to-halo mass ratios. The most informative streams are those with sharp turns or flat segments, since these streams sample a wide range of curvature vectors over a small area (sharp turns) or have a vanishing projected acceleration component (flat segments). We apply our method to low surface brightness imaging of NGC 5907, and find that its dark matter halo is oblate. Our analytic approach is significantly faster than other stream modeling techniques, and indicates which parts of a stream contribute to constraints on the potential. The method enables a measurement of dark matter halo shapes for thousands of systems using stellar stream detections expected from upcoming facilities like Rubin and Roman