David W Hogg

The vertical kinematics of stars near the Sun can be used to measure the total mass distribution near the Galactic disk and to study out-of-equilibrium dynamics. With contemporary stellar surveys, the tracers of vertical dynamics are so numerous and so well measured that the shapes of underlying orbits are almost directly visible in the data through element abundances or even stellar density. These orbits can be used to infer a mass model for the Milky Way, enabling constraints on the dark matter distribution in the inner galaxy. Here we present a flexible model for foliating the vertical position-velocity phase space with orbits, for use in data-driven studies of dynamics. The vertical acceleration profile in the vicinity of the disk, along with the orbital actions, angles, and frequencies for individual stars, can all be derived from that orbit foliation. We show that this framework - "Orbital Torus Imaging" (OTI) - is rigorously justified in the context of dynamical theory, and does a good job of fitting orbits to simulated stellar abundance data with varying degrees of realism. OTI (1) does not require a global model for the Milky Way mass distribution, and (2) does not require detailed modeling of the selection function of the input survey data. We discuss the approximations and limitations of the OTI framework, which currently trades dynamical interpretability for flexibility in representing the data in some regimes, and which also presently separates the vertical and radial dynamics. We release an open-source tool, torusimaging, to accompany this article.

We report on the discovery and validation of a transiting long-period mini-Neptune orbiting a bright (V= 9.0 mag) G dwarf (TOI 4633; R= 1.05 R⊙, M= 1.10 M⊙). The planet was identified in data from the Transiting Exoplanet Survey Satellite by citizen scientists taking part in the Planet Hunters TESS project. Modelling of the transit events yields an orbital period of 271.9445±0.0040 days and radius of 3.2±0.20 R⊕. The Earth-like orbital period and an incident flux of

When there are many observations of an astronomical source - many images with different dithers, or many spectra taken at different barycentric velocities - it is standard practice to shift and stack the data, to (for example) make a high signal-to-noise average image or mean spectrum. Bound-saturating measurements are made by manipulating a likelihood function, where the data are treated as fixed, and model parameters are modified to fit the data. Traditional shifting and stacking of data can be converted into a model-fitting procedure, such that the data are not modified, and yet the output is the shift-adjusted mean. The key component of this conversion is a spectral model that is completely flexible but also a continuous function of wavelength (or position in the case of imaging) that can represent any signal being measured by the device after any reasonable translation (or rotation or field distortion). The benefits of a modeling approach are myriad: The sacred data never are modified. Noise maps, data gaps, and bad-data masks don't require interpolation. The output can take the form of an image or spectrum evaluated on a pixel grid, as is traditional. In addition to shifts, the model can account for line-spread or point-spread function variations, world-coordinate-system variations, and calibration or normalization variations. The noise in the output becomes uncorrelated across neighboring pixels as the shifts deliver good coverage in some sense. The only cost is a small increase in computational complexity over that of traditional methods. We demonstrate the method with a small data example and we provide open source sample code for re-use.

We present AspGap, a new approach to infer stellar labels from low-resolution Gaia XP spectra, including precise [/M] estimates for the first time. AspGap is a neural-network based regression model trained on APOGEE spectra. In the training step, AspGap learns to use XP spectra not only to predict stellar labels but also the high-resolution APOGEE spectra that lead to the same stellar labels. The inclusion of this last model component -- dubbed the hallucinator -- creates a more physically motivated mapping and significantly improves the prediction of stellar labels in the validation, particularly of [/M]. For giant stars, we find cross-validated rms accuracies for Teff, log g, [M/H], [/M] of ~1%, 0.12 dex, 0.07 dex, 0.03 dex, respectively. We also validate our labels through comparison with external datasets and through a range of astrophysical tests that demonstrate that we are indeed determining [/M] from the XP spectra, rather than just inferring it indirectly from correlations with other labels. We publicly release the AspGap codebase, along with our stellar parameter catalog for all giants observed by Gaia XP. AspGap enables new insights into the formation and chemo-dynamics of our Galaxy by providing precise [/M] estimates for 23 million giant stars, including 12 million with radial velocities from Gaia.

Stellar age measurements are fundamental to understanding a wide range of astronomical processes, including galactic dynamics, stellar evolution, and planetary system formation. However, extracting age information from Main Sequence stars is complicated, with techniques often relying on age proxies in the absence of direct measurements. The Gaia data releases have enabled detailed studies of the dynamical properties of stars within the Milky Way, offering new opportunities to understand the relationship between stellar age and dynamics. In this study, we leverage high-precision astrometric data from Gaia DR3 to construct a stellar age prediction model based only on stellar dynamical properties; namely, the vertical action. We calibrate two distinct, hierarchical stellar age--vertical action relations, first employing asteroseismic ages for red giant branch stars, then isochrone ages for main-sequence turn-off stars. We describe a framework called "zoomies" based on this calibration, by which we can infer ages for any star given its vertical action. This tool is open-source and intended for community use. We compare dynamical age estimates from "zoomies" with ages derived from other techniques for a sample of open clusters and main-sequence stars with asteroseismic age measurements. We also compare dynamical age estimates for stellar samples from the Kepler, K2, and TESS exoplanet transit surveys. While dynamical age relations are associated with large uncertainty, they are generally mass-independent and depend on homogeneously measured astrometric data. These age predictions are uniquely useful for large-scale …

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation with multi-frequency methods, where different signals are isolated by their frequency behaviors. Many so-called "blind" methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC is a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that $\textit{do not contain the signal}$, and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds, and show that our ML model can exploit non-Gaussian features, such as point sources and spatially-varying spectral indices, to produce lower-variance maps than …

Under the assumption of a simple and time-invariant gravitational potential, many Galactic dynamics techniques infer the milky Way's mass and dark matter distributions from stellar kinematic observations. These methods typically rely on parameterized potential models of the Galaxy and must take into account nontrivial survey selection effects, because they make use of the density of stars in phase space. Large-scale spectroscopic surveys now supply information beyond kinematics in the form of precise stellar label measurements (especially element abundances). These element abundances are known to correlate with orbital actions or other dynamical invariants. Here, we use the Orbital Torus Imaging framework that uses abundance gradients in phase space to map orbits. In many cases these gradients can be measured without detailed knowledge of the selection function. We use stellar surface abundances from …

Stellar infrared excesses can indicate various phenomena of interest, from protoplanetary disks to debris disks, or (more speculatively) techno-signatures along the lines of Dyson spheres. In this paper, we conduct a large search for such excesses, designed as a data-driven contextual anomaly detection pipeline. We focus our search on FGK stars close to the main sequence to favour non-young host stars. We look for excess in the mid-infrared, unlocking a large sample to search in while favouring extreme IR excess akin to the ones produced by Extreme Debris Disks (EDD). We combine observations from ESA Gaia DR3, 2MASS, and the unWISE of NASA WISE, and create a catalogue of 4,898,812 stars with mag. We consider a star to have an excess if it is substantially brighter in and bands than what is predicted from an ensemble of machine-learning models trained on the data, taking optical and near-infrared information as input features. We apply a set of additional cuts (derived from the ML models and the objects' astronomical features) to avoid false-positive and identify a set of 53 objects (a rate of ), including one previously identified EDD candidate. Typical infrared-excess fractional luminosities we find are in the range 0.005 to 0.1, consistent with known EDDs.