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ABSTRACT We present a distance-resolved reconstruction of the local line-of-sight Galactic magnetic field, $$B_{||}$$, by combining a 3D electron density ($$n_{\mathrm{ e}}$$) map derived from dust map-informed simulations and a full-sky map of Faraday rotation measure (RM). The forward model evaluates RM on the same 3D grid as the $$n_{\mathrm{ e}}$$ map and compares to the Galactic Faraday rotation sky. We infer $$B_{||}$$ with a Gaussian-process prior whose power spectrum is inferred from the data using geometric variational inference. The result is a local (within 1.25 kpc where $$|b| \gt 5^{\circ }$$) map of $$B_{||}$$ with uncertainties. The reconstructed RM sky reproduces prominent features of Faraday rotation sky, with a root mean square average strength of $$B_{||}$$ of $$1.63\pm 0.16\,\rm \mu G$$. In face-on views, the magnetic field exhibits coherent patches with alternating sign and hints of kpc-scale modulations, but with significant structure seen on scales of order 100 pc. The $$B_{||}$$ field is seen to exhibit a 3D power spectrum with an average slope of $$-2.73 \pm 0.19$$. We validate our $$B_{||}$$ reconstruction with Galactic pulsars. Predicted RMs (computed by integrating $$n_{e}B_{||}$$ to each pulsar’s distance) correlates with observed RMs, and predicted dispersion measures (DMs) from the $$n_{e}$$ map also correlate with measured DMs, albeit with significant scatter. 

Abstract Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty estimations and are severely constrained by the parameter space. In this work, we introduce a new approach using normalizing flows—a type of deep generative model—to estimate stellar ages for evolved stars with improved accuracy and robust uncertainty characterization. The model is trained on stellar masses for evolved stars derived from asteroseismology and predicts the relationship between the carbon and nitrogen abundances of a given star and its age. Unlike standard neural network techniques, normalizing flows enable the recovery of full likelihood distributions for individual stellar ages, offering a richer and more informative perspective on uncertainties. Our method yields age estimations for 378,720 evolved stars and achieves a typical absolute age uncertainty of approximately 2 Gyr. By intrinsically accounting for the coverage and density of the training data, our model ensures that the resulting uncertainties reflect both the inherent noise in the data and the completeness of the sampled parameter space. Applying this method to data from the fifth-generation Sloan Digital Sky Survey Milky Way Mapper, we have produced the largest stellar age catalog for evolved stars to date. 

Abstract We present a deep, high-angular-resolution 3D dust map of the southern Galactic plane over 239° < l < 6° and ∣b∣ < 10° built on photometry from the DECaPS2 survey, in combination with photometry from VISTA Variables in the Via Lactea, the Two Micron All Sky Survey, and “Unofficial” Wide-field Infrared Survey Explorer and parallaxes from Gaia Data Release 3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using thebrutusstellar inference framework with a set of theoretical MESA Isochrone and Stellar Tracks (MIST) stellar models. Our resultant 3D dust map has an angular resolution of $1\prime$, roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance ofd ≈ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughlyA_V ≈ 12 mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python packagedustmaps. When combined with the existingBayestar193D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk ∣b∣ < 10°. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia. 

Abstract The Open Cluster Chemical Abundances and Mapping (OCCAM) survey seeks to curate a large, comprehensive, uniform dataset of open clusters and member stars to constrain key Galactic parameters. This eighth entry from the OCCAM survey, based on the newly released Sloan Digital Sky Survey V/Milky Way Mapper Data Release 19 (DR19), has established a sample of 158 quality open clusters that are used to constrain the radial and azimuthal gradients of the Milky Way. The DR19 cluster sample [Fe/H] abundances are largely consistent with measurements from other large-scale spectroscopic surveys. However, the gradients we calculate deviate considerably for some elements. We find an overall linear Galactic radial [Fe/H] gradient of −0.079 ± 0.006 dex kpc⁻¹using the cluster’s current Galactocentric radius and a gradient of −0.071 ± 0.005 dex kpc⁻¹with respect to the cluster’s guiding center radius. We do not find strong evidence for significant evolution of the differential element gradients ([X/Fe]) investigated here (O, Mg, Si, S, Ca, Ti, Cr, Mn, Co, Ni, Na, Al, K, Ce, Nd), and instead show indications that the radial [Fe/H] gradient does not change significantly with stellar population age. For the first time, using the OCCAM sample, we have sufficient numbers of clusters to investigate Galactic azimuthal variations. In this work, we find evidence of azimuthal variations in the measured radial abundance gradient in the Galactic disk using our open cluster sample. 

High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of kpc from the Sun. We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. Our 3D dust map has an angular resolution of up to $ $ ($$N_ side =256$$), and we achieve parsec-scale distance resolution, sampling the dust in $516$ logarithmically spaced distance bins spanning pc . We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package. 

Abstract The fifth-generation Sloan Digital Sky Survey (SDSS-V) is conducting the first all-sky low-resolution spectroscopic survey of the Milky Way’s (MW) stellar halo. We describe the stellar parameter pipeline for the SDSS-V halo survey, which simultaneously models spectra, broadband photometry, and parallaxes to derive stellar parameters, metallicities, alpha abundances, and distances. The resulting Baryon Oscillation Spectroscopic Survey (BOSS)-MINESweepercatalog is validated across a wide range of stellar parameters and metallicities using star clusters and a comparison to high-resolution spectroscopic surveys. We demonstrate several scientific capabilities of this dataset: identifying the most chemically peculiar stars in our Galaxy, discovering and mapping distant halo substructures, and measuring the all-sky dynamics of the MW on the largest scales. The BOSS-MINESweepercatalog for SDSS DR19 is publicly available and will be updated for future data releases. 

Abstract Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components. This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data space (the observed spectrum)—a method we call “Marginalized Analytic Data-space Gaussian Inference for Component Separation” (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not well modeled by synthetic spectra. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight. We reprocess all public Gaia DR3 RVS spectra and present an improved 8621 Å DIB catalog, free of detectable stellar line contamination. We constrain the rest-frame wavelength to 8623.14 ± 0.087 Å (vacuum), find no significant evidence for DIBs in the Local Bubble from the 1/6th of RVS spectra that are public, and show unprecedented correlation with kinematic substructure in Galactic CO maps. We validate the catalog, its reported uncertainties, and biases using synthetic injection tests. We believe MADGICS provides a viable path forward for large-scale spectral line measurements in the presence of complex spectral contamination. 

Abstract Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observedz = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ²value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. 

Abstract Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixel-wise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty of that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey. In addition to removing many >3 σ outliers and improving uncertainty estimates by a factor of ∼2–3 on nebulous fields, we also show that our method is well behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve the flux and flux uncertainty estimates of past as well as future surveys. 

Abstract We presentAugustus, a catalog of distance, extinction, and stellar parameter estimates for 170 million stars from 14 mag <r< 20 mag and with ∣b∣ > 10° drawing on a combination of optical to near-infrared photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from Gaia DR2 and 3D dust extinction maps. After applying quality cuts, we find 125 million objects have “high-quality” posteriors with statistical distance uncertainties of ≲10% for objects with well-constrained stellar types. This is a substantial improvement over the distance estimates derived from Gaia parallaxes alone and in line with the recent results from Anders et al. We find the fits are able to reproduce the dereddened Gaia color–magnitude diagram accurately, which serves as a useful consistency check of our results. We show that we are able to detect large, kinematically coherent substructures in our data clearly relative to the input priors, including the Monoceros Ring and the Sagittarius Stream, attesting to the quality of the catalog. Our results are publicly available at doi:10.7910/DVN/WYMSXV. An accompanying interactive visualization can be found athttp://allsky.s3-website.us-east-2.amazonaws.com.