Stream-orbit misalignment I: The dangers of orbit-fitting

Tidal streams don't, in general, delineate orbits. A stream-orbit misalignment is expected to lead to biases when using orbit-fitting to constrain models for the Galactic potential. In this first of two papers we discuss the expected magnitude of the misalignment and the resulting dangers of using orbit-fitting algorithms to constrain the potential. We summarize data for known streams which should prove useful for constraining the Galactic potential, and compute their actions in a realistic Galactic potential. We go on to discuss the formation of tidal streams in angle-action space, and explain why, in general, streams do not delineate orbits. The magnitude of the stream-orbit misalignment is quantified for a logarithmic potential and a multi-component Galactic potential. Specifically, we focus on the expected misalignment for the known streams. By introducing a two-parameter family of realistic Galactic potentials we demonstrate that assuming these streams delineate orbits can lead to order one errors in the halo flattening and halo-to-disc force ratio at the Sun. We present a discussion of the dependence of these results on the progenitor mass, and demonstrate that the misalignment is mass-independent for the range of masses of observed streams. Hence, orbit-fitting does not yield better constraints on the potential if one uses narrower, lower-mass streams.Comment: 13 pages, 7 figures, accepted for publication in MNRA

Stream-orbit misalignment II: A new algorithm to constrain the Galactic potential

In the first of these two papers we demonstrated that assuming streams delineate orbits can lead to order one errors in potential parameters for realistic Galactic potentials. Motivated by the need for an improvement on orbit-fitting, we now present an algorithm for constraining the Galactic potential using tidal streams without assuming that streams delineate orbits. This approach is independent of the progenitor mass so is valid for all observed tidal streams. The method makes heavy use of angle-action variables and seeks the potential which recovers the expected correlations in angle space. We demonstrate that the method can correctly recover the parameters of a simple two-parameter logarithmic potential by analysing an N-body simulation of a stream. We investigate the magnitude of the errors in observational data for which the method can still recover the correct potential and compare this to current and future errors in data. The errors in the observables of individual stars for current and near future data are shown to be too large for the direct use of this method, but when the data are averaged in bins on the sky, the resulting averaged data are accurate enough to constrain correctly the potential parameters for achievable observational errors. From pseudo-data with errors comparable to those that will be furnished in the era of Gaia (20 per cent distance errors, 1.2 mas/yr proper motion errors, and 10 km/s line-of-sight velocity errors) we recover the circular velocity, V_c=220 km/s, and the flattening of the potential, q=0.9, to be V_c=223+/-10km/s and q=0.91+/-0.09.Comment: 11 pages, 5 figures, accepted for publication in MNRA

The period--luminosity relation for Mira variables in the Milky Way using Gaia DR3: a further distance anchor for H0H_0

Gaia DR3 parallaxes are used to calibrate preliminary period--luminosity relations of O-rich Mira variables in the 2MASS $J$, $H$ and $K_s$ bands using a probabilistic model accounting for variations in the parallax zeropoint and underestimation of the parallax uncertainties. The derived relations are compared to those measured for the Large and Small Magellanic Clouds, the Sagittarius dwarf spheroidal galaxy, globular cluster members and the subset of Milky Way Mira variables with VLBI parallaxes. The Milky Way linear $JHK_s$ relations are slightly steeper and thus fainter at short period than the corresponding LMC relations suggesting population effects in the near-infrared are perhaps larger than previous observational works have claimed. Models of the Gaia astrometry for the Mira variables suggest that, despite the intrinsic photocentre wobble and use of mean photometry in the astrometric solution of the current data reduction, the recovered parallaxes should be on average unbiased but with underestimated uncertainties for the nearest stars. The recommended Gaia EDR3 parallax zeropoint corrections evaluated at $\nu_\mathrm{eff}=1.25\,\mu\mathrm{m}^{-1}$ require minimal ($\lesssim5\,\mu\mathrm{as}$) corrections for redder five-parameter sources, but over-correct the parallaxes for redder six-parameter sources, and the parallax uncertainties are underestimated, at most by a factor $\sim1.6$ at $G\approx12.5\,\mathrm{mag}$. The derived period--luminosity relations are used as anchors for the Mira variables in the Type Ia host galaxy NGC 1559 to find $H_0=(73.7\pm4.4)\,\mathrm{km\,s}^{-1}\mathrm{Mpc}^{-1}$.Comment: 31 pages, 18 figures, accepted for publication in MNRA

Hunting for C-rich long-period variable stars in the Milky Way's bar-bulge using unsupervised classification of Gaia BP/RP spectra

The separation of oxygen- and carbon-rich AGB sources is crucial for their accurate use as local and cosmological distance and age/metallicity indicators. We investigate the use of unsupervised learning algorithms for classifying the chemistry of long-period variables from Gaia DR3's BP/RP spectra. Even in the presence of significant interstellar dust, the spectra separate into two groups attributable to O-rich and C-rich sources. Given these classifications, we utilise a supervised approach to separate O-rich and C-rich sources without BP/RP spectra but instead given broadband optical and infrared photometry finding a purity of our C-rich classifications of around $95$ per cent. We test and validate the classifications against other advocated colour-colour separations based on photometry. Furthermore, we demonstrate the potential of BP/RP spectra for finding S-type stars or those possibly symbiotic sources with strong emission lines. Although our classification suggests the Galactic bar-bulge is host to very few C-rich long-period variable stars, we do find a small fraction of C-rich stars with periods $>250\,\mathrm{day}$ that are spatially and kinematically consistent with bar-bulge membership. We argue the combination of the observed number, the spatial alignment, the kinematics and the period distribution disfavour young metal-poor star formation scenarios either in situ or in an accreted host, and instead, these stars are highly likely to be the result of binary evolution and the evolved versions of blue straggler stars already observed in the bar-bulge.Comment: 20 pages, 13 figures, accepted for publication in MNRA

A kinematic calibration of the O-rich Mira variable period–age relation from Gaia

Empirical and theoretical studies have demonstrated that the periods of Mira variable stars are related to their ages. This, together with their brightness in the infrared, makes them powerful probes of the formation and evolution of highly-extincted or distant parts of the Local Group. Here we utilize the Gaia DR3 catalogue of long-period variable candidates to calibrate the period–age relation of the Mira variables. Dynamical models are fitted to the O-rich Mira variable population across the extended solar neighbourhood and then the resulting solar neighbourhood period–kinematic relations are compared to external calibrations of the age–kinematic relations to derive a Mira variable period–age relation of $\tau \approx (6.9\pm 0.3)\, \mathrm{Gyr}(1+\tanh ((330\, \mathrm{d}-P)/(400\pm 90)\mathrm{d})$. Our results compare well with previous calibrations using smaller data sets as well as the period–age properties of Local Group cluster members. This calibration opens the possibility of accurately characterizing the star formation and the impact of different evolutionary processes throughout the Local Group

A machine learning approach to photometric metallicities of giant stars

Despite the advances provided by large-scale photometric surveys, stellar features – such as metallicity – generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photoastrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilizing the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm 0.19\, \mathrm{dex}$. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient (−0.528 ± 0.002 dex kpc−1) and the vertex deviation of the bar ($-21.29\pm 2.74\, \mathrm{deg}$)