The Sun has been found to be depleted in refractory (rock-forming) elements relative to nearby solar analogs, suggesting a potential indicator of planet formation. Given the small amplitude of the depletion, previous analyses have primarily relied on high signal-to-noise stellar spectra and a strictly differential approach to determine elemental abundances. We present an alternative, likelihood-based approach that can be applied to much larger samples of stars with lower precision abundance determinations. We utilize measurements of about 1700 solar analogs from the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) and the APOGEE Stellar Parameter and Chemical Abundance Pipeline (ASPCAP DR16). By developing a hierarchical mixture model for the data, we place constraints on the statistical properties of the elemental abundances, including correlations with condensation temperature and the fraction of stars with refractory element depletions. We find evidence for two distinct populations: a depleted population of stars that makes up the majority of solar analogs including the Sun, and a not-depleted population that makes up between ∼10% and 30% of our sample. We find correlations with condensation temperature generally in agreement with higher precision surveys of a smaller sample of stars. Such trends, if robustly linked to the formation of planetary systems, provide a means to connect stellar chemical abundance patterns to planetary systems over large samples of Milky Way stars.

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 Δβ d ≲ 0.01 and thus test models of dust composition that predict that β 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 nonexistence of exo-Oort clouds at roughly 2.9σ 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° patches for all lines of sight with N H ≳ 2 × 1020 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. 37 37 A supplement describing author contributions to this paper can be found at https://simonsobservatory.org/wp-content/uploads/2022/02/SO_GS_Contributions.pdf.

We present a data-driven method for reconstructing the galactic acceleration field from phase-space (position and velocity) 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, as 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, and 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.

Thermal emission from debris disks around stars has been measured using targeted and resolved observations. We present an alternative, likelihood-based approach in which temperature maps from the Planck CMB survey at 857 and 545 GHz are analyzed in conjunction with stellar positions from Gaia to estimate the fraction of stars hosting disks and the thermal emission from the disks. The debris disks are not resolved (or even necessarily detected individually) but their statistical properties and the correlations with stellar properties are measured for several thousand stars. We compare our findings with higher sensitivity surveys of smaller samples of stars. For dimmer stars, in particular K and M-dwarfs, we find about 10 percent of stars within 80 pc have emission consistent with debris disks. We also report on 80 candidate disks, the majority of which are not previously identified. We have previously constrained the properties of Exo-Oort clouds using Planck data -with future CMB surveys both components can be measured for different stellar types, providing a new avenue to study the outer parts of planetary systems.