We develop a novel Bayesian methodology aimed at reliably and precisely inferring the distribution of dark matter within the Milky Way using rotation curve data. We identify a subset of the available rotation curve tracers that are mutually consistent with each other, thus eliminating data sets that might suffer from systematic bias. We investigate three different models for the mass distribution of the luminous (baryonic) component that bracket the range of likely morphologies. We demonstrate the statistical performance of our method on simulated data in terms of coverage, fractional distance, and mean squared error. Applying it to Milky Way data we measure the local dark matter density at the solar circle $ρ_0$ to be $ρ_0 = 0.43\pm 0.02(\rm{stat})\pm0.01(\rm{sys})$ GeV/cm$^3$, with an accuracy $\sim$ 6%. This result is robust to the assumed baryonic morphology. The scale radius and inner slope of the dark matter profile are degenerate and cannot be individually determined with high accuracy. We show that these results are robust to several possible residual systematic errors in the rotation curve data.
This work investigates the vertical profile of the stars in the disk of the Milky Way. The models investigated are of the form $sech^{2/n}(nz/(2H))$ where, setting $α= 2/n$, the three functions of the sequence $α= 0,1,2$ correspond to exponential, $sech$, $sech^2$ functions. We consider symmetric models and asymmetric models, above and below the plane. The study uses the large sample of K and M stars of Ferguson et al. (2017) and applies the methods of Bayesian model comparison to discriminate between the 6 models. Two inconsistencies in Ferguson et al. (2017), concerning the vertical height c…
We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie, Harris, & Widrow (2015) and Eadie & Harris (2016) and builds upon the preliminary reports by Eadie et al (2015a,c). The method uses a distribution function $f(\mathcal{E},L)$ to model the galaxy and kinematic data from satellite objects such as globular clusters (GCs) to trace the Galaxy's gravitational potential. A major advantage of the method is that it not only includes complete and incomp…
We present mass and mass profile estimates for the Milky Way Galaxy using the Bayesian analysis developed by Eadie et al (2015b) and using globular clusters (GCs) as tracers of the Galactic potential. The dark matter and GCs are assumed to follow different spatial distributions; we assume power-law model profiles and use the model distribution functions described in Evans et al. (1997); Deason et al (2011, 2012a). We explore the relationships between assumptions about model parameters and how these assumptions affect mass profile estimates. We also explore how using subsamples of the GC popula…
We develop a Bayesian Machine Learning framework called BINGO (Bayesian INference for Galactic archaeOlogy) centred around a Bayesian neural network. After being trained on the APOGEE and \emph{Kepler} asteroseismic age data, BINGO is used to obtain precise relative stellar age estimates with uncertainties for the APOGEE stars. We carefully construct a training set to minimise bias and apply BINGO to a stellar population that is similar to our training set. We then select the 17,305 stars with ages from BINGO and reliable kinematic properties obtained from \textit{Gaia} DR2. By combining the a…
The exponential scale length ($L_d$) of the Milky Way's (MW's) disk is a critical parameter for describing the global physical size of our Galaxy, important both for interpreting other Galactic measurements and helping us to understand how our Galaxy fits into extragalactic contexts. Unfortunately, current estimates span a wide range of values and often are statistically incompatible with one another. Here, we perform a Bayesian meta-analysis to determine an improved, aggregate estimate for $L_d$, utilizing a mixture-model approach to account for the possibility that any one measurement has no…
We present improved estimates of several global properties of the Milky Way, including its current star formation rate (SFR), the stellar mass contained in its disk and bulge+bar components, as well as its total stellar mass. We do so by combining previous measurements from the literature using a hierarchical Bayesian (HB) statistical method that allows us to account for the possibility that any value may be incorrect or have underestimated errors. We show that this method is robust to a wide variety of assumptions about the nature of problems in individual measurements or error estimates. Ult…
Space observatories like the Hubble Space Telescope and Gaia are providing unprecedented 6D phase space information of satellite galaxies. Such measurements can shed light on the structure and assembly history of the Local Group, but improved statistical methods are needed to use them efficiently. Here we illustrate such a method using analogs of the Local Group's two most massive satellite galaxies, the Large Magellanic Cloud (LMC) and Triangulum (M33), from the Illustris dark-matter-only cosmological simulation. We use a Bayesian inference scheme combining measurements of positions, velociti…
Globular clusters in \textsc{OrbIT}: complete dynamical characterisation of the globular cluster population of the Milky Way through updated orbital reconstruction
Università degli Studi di Bologna（博洛尼亚大学）；INAF（意大利国家天体物理研究所）；Pontificia Universidad Católica de Chile（智利天主教 Pontificia 大学）；Instituto Milenio de Astrofísica MAS（MAS 天体物理研究所）；Université Côte d’Azur（蔚蓝海岸大学）；Observatoire de la Côte d’Azur（蔚蓝海岸天文台）
The mass of the Milky Way is a critical quantity which, despite decades of research, remains uncertain within a factor of two. Until recently, most studies have used dynamical tracers in the inner regions of the halo, relying on extrapolations to estimate the mass of the Milky Way. In this paper, we extend the hierarchical Bayesian model applied in Eadie & Jurić (2019) to study the mass distribution of the Milky Way halo; the new model allows for the use of all available 6D phase-space measurements. We use kinematic data of halo stars out to $142~{\rm kpc}$, obtained from the H3 Survey and $\t…
We test a Yukawa correction to the Newtonian potential, making use of our own Galaxy - the Milky Way - as a testbed. We include as free parameter the Yukawa strength and range and the dark matter profile parameters, and compare several morphologies for the bulge, gas, and disk components, also using Bayesian model selection criteria. We employ up-to-date datasets for both the visible (baryonic) component of the Milky Way, and for the tracers of the gravitational potential (the Rotation Curve). We find that the data are consistent with the Newtonian potential, and constrain the Yukawa coupling …
We present BALRoGO: Bayesian Astrometric Likelihood Recovery of Galactic Objects, a public code to measure the centers, effective radii, and bulk proper motions of Milky Way globular clusters and Local Group dwarf spheroidals, whose data are mixed with Milky Way field stars. Our approach presents innovative methods such as surface density fits allowing for strong interloper contamination and proper motion fits using a Pearson VII distribution for interlopers, instead of classic Gaussian-mixture recipes. We also use non-parametric approaches to represent the color-magnitude diagram of such stel…
We employ our Bayesian Machine Learning framework BINGO (Bayesian INference for Galactic archaeOlogy) to obtain high-quality stellar age estimates for 68,360 red giant and red clump stars present in the 17th data release of the Sloan Digital Sky Survey, the APOGEE-2 high-resolution spectroscopic survey. By examining the denoised age-metallicity relationship of the Galactic disc stars, we identify a drop in metallicity with an increase in [Mg/Fe] at an early epoch, followed by a chemical enrichment episode with increasing [Fe/H] and decreasing [Mg/Fe]. This result is congruent with the chemical…
We present the results of a new, non-parametric method to reconstruct the Galactic dark matter profile directly from observations. Using the latest kinematic data to track the total gravitational potential and the observed distribution of stars and gas to set the baryonic component, we infer the dark matter contribution to the circular velocity across the Galaxy. The radial derivative of this dynamical contribution is then estimated to extract the dark matter profile. The innovative feature of our approach is that it makes no assumption on the functional form nor shape of the profile, thus all…
In a series of three papers, Eadie et al. developed a hierarchical Bayesian method to estimate the Milky Way Galaxy's mass given a physical model for the potential, a measurement model, and kinematic data of test particles such as globular clusters (GCs) or halo stars in the Galaxy's halo. The Galaxy's virial mass was found to have a 95\% Bayesian credible region (c.r.) of $(0.67, 1.09) \times 10^{12} M_{\odot}$. In the present study, we test the hierarchical Bayesian method against simulated galaxies created in the McMaster Unbiased Galaxy Simulations 2 (MUGS2), for which the true mass is kno…
We introduce a comprehensive analysis of multi-epoch stellar line-of-sight velocities to determine the intrinsic velocity dispersion of the ultrafaint satellites of the Milky Way. Our method includes a simultaneous Bayesian analysis of both membership probabilities and the contribution of binary orbital motion to the observed velocity dispersion within a 14-parameter likelihood. We apply our method to the Segue 1 dwarf galaxy and conclude that Segue 1 is a dark-matter-dominated galaxy at high probability with an intrinsic velocity dispersion of 3.7^{+1.4}_{-1.1} km/sec. The dark matter halo re…
We present new mass estimates and cumulative mass profiles (CMPs) with Bayesian credible regions for the Milky Way (MW) Galaxy, given the kinematic data of globular clusters as provided by (1) the $\textit{Gaia}$ DR2 collaboration and the HSTPROMO team, and (2) the new catalog in Vasiliev (2019). We use globular clusters beyond 15kpc to estimate the CMP of the MW, assuming a total gravitational potential model $Φ(r) = Φ_{\circ}r^{-γ}$, which approximates an NFW-type potential at large distances when $γ=0.5$. We compare the resulting CMPs given data sets (1) and (2), and find the results to be …
We present a new estimate of the mass of the Milky Way, inferred via a Bayesian approach by making use of tracers of the circular velocity in the disk plane and stars in the stellar halo, as from the publicly available {\tt galkin} compilation. We use the rotation curve method to determine the dark matter distribution and total mass under different assumptions for the dark matter profile, while the total stellar mass is constrained by surface stellar density and microlensing measurements. We also include uncertainties on the baryonic morphology via Bayesian model averaging, thus converting a p…
Forward-modelling Milky Way Cepheids: selection effects and physical priors in the Gaia-HST calibration
University of Oxford（牛津大学）；Space Telescope Science Institute（太空望远镜科学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Portsmouth（朴茨茅斯大学）；CNRS & Sorbonne Université（法国国家科学研究中心与索邦大学）；Duke University（杜克大学）
Chemo-dynamical reconstruction of Milky Way globular cluster progenitors: Age-metallicity relations and the universality of multiple stellar populations
Dipartimento di Fisica e Astronomia “Augusto Righi”–Università di Bologna（博洛尼亚大学）；INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna（意大利国家天体物理研究所博洛尼亚天文与空间科学观测站）；Berkeley Center for Cosmological Physics and Department of Physics, University of California, Berkeley（加州大学伯克利分校）；ICC, University of Barcelona（巴塞罗那大学）；ICREA（加泰罗尼亚高级研究学院）
Tidal streams provide a powerful tool by means of which the matter distribution of the dark matter halos of their host galaxies can be studied. However, the analysis is not straightforward because streams do not delineate orbits, and for most streams, especially those in external galaxies, kinematic information is absent. We present a method wherein streams are fit with simple corrections made to possible orbits of the progenitor, using a Bayesian technique known as Parallel Tempering to efficiently explore the parameter space. We show that it is possible to constrain the shape of the host hal…
