Weighing the Galactic disk in sub-regions of the solar neighbourhood using Gaia DR2

Abstract

Aims. We infer the gravitational potential of the Galactic disk by analysing the phase-space densities of 120 stellar samples in 40 spatially separate sub-regions of the solar neighbourhood, using Gaia’s second data release (DR2), in order to quantify spatially dependent systematic effects that bias this type of measurement. Methods. The gravitational potential was inferred under the assumption of a steady state in the framework of a Bayesian hierarchical model. We performed a joint fit of our stellar tracers’ three-dimensional velocity distribution, while fully accounting for the astrometric uncertainties of all stars as well as dust extinction, and we also masked angular areas of known open clusters. The inferred gravitational potential is compared, post-inference, to a model for the baryonic matter and halo dark matter components. Results. We see an unexpected but clear trend for all 40 spatially separate sub-regions: Compared to the potential derived from the baryonic model, the inferred gravitational potential is significantly steeper close to the Galactic mid-plane (≲60 pc), but flattens such that the two agree well at greater distances (∼400 pc). The inferred potential implies a total matter density distribution that is highly concentrated to the Galactic mid-plane and decays quickly with height. We see a dependence on the Galactic radius that is consistent with a disk scale length of a few kiloparsecs. Apart from this, there are discrepancies between stellar samples, implying spatially dependent systematic effects which are, at least in part, explained by substructures in the phase-space distributions. Conclusions. In terms of the inferred matter density distribution, the very low matter density that is inferred at greater heights (≳300 pc) is inconsistent with the observed scale height and matter distribution of the stellar disk, which cannot be explained by a misunderstood density of cold gas or other hidden mass. Our interpretation is that these results must be biased by a time-varying phase-space structure, possibly a breathing mode, that is large enough to affect all stellar samples in the same manner.

Weighing the Galactic disk using phase-space spirals II. Most stringent constraints to a thin dark disk using Gaia EDR3

“…Such a disk has been constrained in dynamical mass measurements of the solar neighbourhood (Kramer & Randall 2016b;Caputo et al 2018;Schutz et al 2018;Buch et al 2019), where the strongest constraints use Gaia observations of the very local population of stars. However, in a similar study by Widmark et al (2021a) (also drawing from the results of Widmark 2019), they argue that such measurements are strongly biased by time-varying dynamical effects.…”

“…We used the baryonic model from Schutz et al (2018), based on the pre-Gaia studies by Flynn et al (2006), McKee et al (2015), and Kramer & Randall (2016a). This baryonic model is also used in other local dynamical mass measurements, such as Sivertsson et al (2018), Buch et al (2019), Widmark (2019), and Widmark et al (2021a). In this model, the total baryonic density is a sum of twelve different components: four gas components (molecular, cold atomic, warm atomic, and hot ionised gas); six stellar components divided by absolute magnitude; white dwarfs; and brown dwarfs.…”

“…It is important to note that this model of baryonic matter densities in the solar neighbourhood could potentially suffer from significant systematic errors. For the stellar components, the assumption of iso-thermality are not ideal descriptions when comparing with Gaia data: the stellar components' vertical velocity distributions are not strictly Gaussian but have heavier tails and the shape of the stellar number density profiles differ from those predicted by the baryonic model (a comparison can be found in the appendix of Widmark et al 2021a). Perhaps even more worrisome are the matter density distributions of the gas components, which have larger statistical uncertainties and are arguably more prone to significant systematic bias.…”

Charting galactic accelerations II: how to 'learn' accelerations in the solar neighbourhood

“…While velocity dispersions are comparatively easy to measure from kinematic data, we lose much of the information content of the data compared with techniques working directly with the DF. For this reason, many studies have instead adopted the latter approach, typically in one dimension (e.g., Schutz et al 2018;Buch et al 2019;Widmark & Monari 2019;Widmark 2019;Widmark et al 2021a;Li & Widrow 2021). Note that either treatment necessitates the assumption of dynamical equilibrium, so that the time-derivative term in equation ( 1) can be neglected.…”

“…We calculate radial and vertical accelerations within ∼ 1 kpc of the Sun from a mock dataset in Section 4. Then, Section 5 compares the accuracy of our measured accelerations against those produced using other techniques, specifically the Jeans analysis method of Salomon et al (2020) and the 1D DF fitting method of Widmark et al (2021a). Finally, Section 6 provides a discussion and concluding remarks.…”