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The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of R ∼ 22,500. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m s ^−1 and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multifiber spectrographs. After calibration, we achieve 47 m s ^−1 RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in the Sloan Digital Sky Survey V to improve the fiber-unified RV catalog.

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.

Time-varying inhomogeneities on stellar surfaces constitute one of the largest sources of radial velocity (RV) error for planet detection and characterization. We show that stellar variations, because they manifest on coherent, rotating surfaces, give rise to changes that are complex but usably compact and coherent in the spectral domain. Methods for disentangling stellar signals in RV measurements benefit from modeling the full domain of spectral pixels. We simulate spectra of spotted stars using starry and construct a simple spectrum projection space that is sensitive to the orientation and size of stellar surface features. Regressing measured RVs in this projection space reduces RV scatter by 60%–80% while preserving planet shifts. We note that stellar surface variability signals do not manifest in spectral changes that are purely orthogonal to a Doppler shift or exclusively asymmetric in line profiles; enforcing orthogonality or focusing exclusively on asymmetric features will not make use of all the information present in the spectra. We conclude with a discussion of existing and possible implementations on real data based on the presented compact, coherent framework for stellar signal mitigation.

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 age measurements from open clusters and asteroseismology. We use zoomies to generate and compare dynamical age estimates for stars 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 demographic investigations, especially in disentangling the relationship between planet occurrence, metallicity, and age for low-mass stars.

The element abundance pattern found in Milky Way disk stars is close to two-dimensional, dominated by production from one prompt process and one delayed process. This simplicity is remarkable, since the elements are produced by a multitude of nucleosynthesis mechanisms operating in stars with a wide range of progenitor masses. We fit the abundances of 14 elements for 48,659 red-giant stars from APOGEE Data Release 17 using a flexible, data-driven K -process model—dubbed KPM . In our fiducial model, with K = 2, each abundance in each star is described as the sum of a prompt and a delayed process contribution. We find that KPM with K = 2 is able to explain the abundances well, recover the observed abundance bimodality, and detect the bimodality over a greater range in metallicity than has previously been possible. We compare to prior work by Weinberg et al., finding that KPM produces similar results, but that KPM better predicts stellar abundances, especially for the elements C+N and Mn and for stars at supersolar metallicities. The model fixes the relative contribution of the prompt and delayed processes to two elements to break degeneracies and improve interpretability; we find that some of the nucleosynthetic implications are dependent upon these detailed choices. We find that moving to four processes adds flexibility and improves the model’s ability to predict the stellar abundances, but does not qualitatively change the story. The results of KPM will help us to interpret and constrain the formation of the Galaxy disk, the relationship between abundances and ages, and the physics of nucleosynthesis.

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 the Apache Point Observatory Galactic Evolution Experiment survey combined with kinematic data from the Gaia mission. Our method reveals the vertical ( z -direction) orbital structure in the Galaxy and enables empirical measurements of the vertical acceleration field and orbital frequencies in the disk. From these measurements, we infer the total surface mass density, Σ, and midplane volume density, ρ _0 , as a function of Galactocentric radius and height. Around the Sun, we find ${{\rm{\Sigma }}}_{\odot }(z=1.1\,\mathrm{kpc})={72}_{-9}^{+6}\,{M}_{\odot }\,{\mathrm{pc}}^{-2}$ and ${\rho }_{\odot }(z=0)={0.081}_{-0.009}^{+0.015}\,{M}_{\odot }\,{\mathrm{pc}}^{-3}$ using the most constraining abundance ratio, [Mg/Fe]. This corresponds to a dark matter contribution in surface density of Σ _⊙,DM ( z = 1.1 kpc) = 24 ± 4 M _⊙ pc ^−2 , and in total volume mass density of ρ _⊙,DM ( z = 0) = 0.011 ± 0.002 M _⊙ pc ^−3 . Moreover, using these mass density values we estimate the scale length of the low- α disk to be h _R = 2.24 ± 0.06 kpc.

We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright ( G < 20.0) confirmed quasars have discrepant redshift estimates (∣Δ z /(1 + z )∣ > 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k -Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G < 20.0 sample with only 6% (10%) catastrophic errors with ∣Δ z /(1 + z )∣ > 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at https://doi.org/10.5281/zenodo.10403370 .

We present AspGap, a new approach to inferring stellar labels from the low-resolution Gaia XP spectra, including precise [ α /M] estimates—the first time these are obtained by such an approach. AspGap is a neural-network-based regression model trained on APOGEE spectra. In the training step, AspGap learns to use not only XP spectra 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 that of [ α /M]. For giant stars, we find cross-validated rms accuracies for T _eff , log g , [M/H], and [ α /M] of ∼1%, 0.12 dex, 0.07 dex, and 0.03 dex, respectively. We also validate our labels through comparison with external data sets and through a range of astrophysical tests that demonstrate that we are indeed determining [ α /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 the discovery of new insights into the formation and chemodynamics of our Galaxy by providing precise [ α /M] estimates for 37 million giant stars, including 14 million with radial velocities from Gaia.

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 ${1.56}_{-0.16}^{+0.20}$ F _⊕ places it in the optimistic habitable zone around the star. Doppler spectroscopy of the system allowed us to place an upper mass limit on the transiting planet and revealed a non-transiting planet candidate in the system with a period of 34.15 ± 0.15 days. Furthermore, the combination of archival data dating back to 1905 with new high angular resolution imaging revealed a stellar companion orbiting the primary star with an orbital period of around 230 yr and an eccentricity of about 0.9. The long period of the transiting planet, combined with the high eccentricity and close approach of the companion star makes this a valuable system for testing the formation and stability of planets in binary systems.

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 “extreme” infrared excesses, designed as a data-driven contextual anomaly detection pipeline. We focus our search on FGK stars close to the main sequence to favor nonyoung host stars. We look for excess in the mid-infrared, unlocking a large sample to search in while favoring extreme IR excess akin to the ones produced by extreme debris disks (EDDs) and/or planetary collision events. We combine observations from ESA Gaia Data Release 3, the Two Micron All-Sky Survey, and the unWISE version of NASA’s Wide-field Infrared Survey Explorer (WISE), and create a catalog of 4,898,812 stars with G < 16 mag. We consider a star to have an excess if it is substantially brighter in the W 1 and W 2 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 machine learning models and the objects’ astronomical features) to avoid false positives and identify a set of 53 objects, including one previously identified EDD candidate. The typical infrared-excess fractional luminosities we find are in the range 0.005–0.1, consistent with previous EDD candidates and potential planetary collision events.

The reversible lipid modification protein S-palmitoylation can dynamically modify the localization, diffusion, function, conformation and physical interactions of substrate proteins. Dysregulated S-palmitoylation is associated with a multitude of human diseases including brain and metabolic disorders, viral infection and cancer. However, the diverse expression patterns of the genes that regulate palmitoylation in the broad range of human cell types are currently unexplored, and their expression in commonly used cell lines that are the workhorse of basic and preclinical research are often overlooked when studying palmitoylation dependent processes. We therefore created CellPalmSeq (https://cellpalmseq.med.ubc.ca), a curated RNAseq database and interactive webtool for visualization of the expression patterns of the genes that regulate palmitoylation across human single cell types, bulk tissue, cancer cell lines and commonly used laboratory non-human cell lines. This resource will allow exploration of these expression patterns, revealing important insights into cellular physiology and disease, and will aid with cell line selection and the interpretation of results when studying important cellular processes that depend on protein S-palmitoylation.

Teneurin C-terminal associated peptides (TCAP) are natural bioactive peptides that possess anxiety-reducing roles in animals, in vivo, and increase cell viability, in vitro. Although these peptides have some primary structural similarity to corticotropin-releasing factor (CRF), they are derived from the distal extracellular region of the teneurin transmembrane protein where they may act as separate soluble peptides after auto-catalytic cleavage from the teneurin protein following interaction with the cognate teneurin receptor, latrophilin (ADGRL), or expressed as a separate mRNA. However, although the signal transduction mechanism of TCAP in neurons has not been established, previous studies indicate an association with the intracellular calcium flux. Therefore, in this study, we have characterized the TCAP-mediated calcium response in hypothalamic cell lines using single-cell calcium methods with pharmacological antagonists to identify potential calcium channels, in vitro. Under normal circumstances, TCAP-1 reduces cytosolic calcium concentrations by uptake into the mitochondria and efflux through the plasma membrane independently of the teneurins. In doing so, TCAP-1 could inhibit the potential ‘stress’ -inducing actions of CRF.

Skeletal muscle regulation is responsible for voluntary muscular movement in vertebrates. The genes of two essential proteins, teneurins and latrophilins (LPHN), evolving in ancestors of multicellular animals form a ligand-receptor pair, and are now shown to be required for skeletal muscle function. Teneurins possess a bioactive peptide, termed the teneurin C-terminal associated peptide (TCAP) that interacts with the LPHNs to regulate skeletal muscle contractility strength and fatigue by an insulin-independent glucose importation mechanism in rats. CRISPR-based knockouts and siRNA-associated knockdowns of LPHN-1 and-3 in the C2C12 mouse skeletal cell line shows that TCAP stimulates an LPHN-dependent cytosolic Ca2+ signal transduction cascade to increase energy metabolism and enhance skeletal muscle function via increases in type-1 oxidative fiber formation and reduce the fatigue response. Thus, the teneurin/TCAP-LPHN system is presented as a novel mechanism that regulates the energy requirements and performance of skeletal muscle.

Protein S-palmitoylation is a reversible post-translational lipid modification that plays a critical role in neuronal development and plasticity, while dysregulated S-palmitoylation underlies a number of severe neurological disorders. Dynamic S-palmitoylation is regulated by a large family of ZDHHC palmitoylating enzymes, their accessory proteins, and a small number of known de-palmitoylating enzymes. Here, we curated and analyzed expression data for the proteins that regulate S-palmitoylation from publicly available RNAseq datasets, providing a comprehensive overview of their distribution in the mouse nervous system. We developed a web-tool that enables interactive visualization of the expression patterns for these proteins in the nervous system (http://brainpalmseq.med.ubc.ca/), and explored this resource to find region and cell-type specific expression patterns that give insight into the function of palmitoylating and de-palmitoylating enzymes in the brain and neurological disorders. We found coordinated expression of ZDHHC enzymes with their accessory proteins, de-palmitoylating enzymes and other brain-expressed genes that included an enrichment of S-palmitoylation substrates. Finally, we utilized ZDHHC expression patterns to predict and validate palmitoylating enzyme-substrate interactions.

The eighteenth data release (DR18) of the Sloan Digital Sky Survey (SDSS) is the first one for SDSS-V, the fifth generation of the survey. SDSS-V comprises three primary scientific programs or “Mappers”: the Milky Way Mapper (MWM), the Black Hole Mapper (BHM), and the Local Volume Mapper. This data release contains extensive targeting information for the two multiobject spectroscopy programs (MWM and BHM), including input catalogs and selection functions for their numerous scientific objectives. We describe the production of the targeting databases and their calibration and scientifically focused components. DR18 also includes ∼25,000 new SDSS spectra and supplemental information for X-ray sources identified by eROSITA in its eFEDS field. We present updates to some of the SDSS software pipelines and preview changes anticipated for DR19. We also describe three value-added catalogs (VACs) based on SDSS-IV data that have been published since DR17, and one VAC based on the SDSS-V data in the eFEDS field.

We present a new approach to measuring the thickness of a partially face-on stellar disk, using dust geometry. In a moderately-inclined disk galaxy, the fraction of reddened stars is expected to be 50% everywhere, assuming that dust lies in a thin midplane. In a thickened disk, however, a wide range of radii project onto the line of sight. Assuming stellar density declines with radius, this geometrical projection leads to differences in the numbers of stars on the near and far sides of the thin dust layer. The fraction of reddened stars will thus differ from the 50% prediction, with a deviation that becomes larger for puffier disks. We map the fraction of reddened red giant branch (RGB) stars across M31, which shows prominent dust lanes on only one side of the major axis. The fraction of reddened stars varies systematically from 20% to 80%, which requires that these stars have an exponential scale height h _z that is 0.14 ± 0.015 times the exponential scale length ( h _r ≈ 5.5 kpc). M31's RGB stars must therefore have h _z = 770 ± 80 pc, which is far thicker than the Milky Way’s thin disk, but comparable to its thick disk. The lack of a significant thin disk in M31 is unexpected, but consistent with its interaction history and high disk velocity dispersion. We suggest that asymmetric reddening be used as a generic criterion for identifying “thick disk”-dominated systems, and discuss prospects for future 3D tomographic mapping of the gas and stars in M31.

Peptides play a major role in the transmission of information to and from the central nervous system. However, because of their structural complexity, the development of pharmacological peptide-based therapeutics has been challenged by the lack of understanding of endogenous peptide evolution. The teneurin C-terminal associated peptides (TCAP) possess many of the required attributes of a practical peptide therapeutic. TCAPs, associated with the teneurin transmembrane proteins that bind to the latrophilins, members of the Adhesion family of G-protein-coupled receptors (GPCR). Together, this ligand-receptor unit plays an integral role in synaptogenesis, neurological development, and maintenance, and is present in most metazoans. TCAP has structural similarity to corticotropin-releasing factor (CRF), and related peptides, such as calcitonin and the secretin-based peptides and inhibits the (CRF)-associated stress response. Latrophilins are structurally related to the secretin family of GPCRs. TCAP is a soluble peptide that crosses the blood-brain barrier and regulates glucose transport into the brain. We posit that TCAP represents a phylogenetically older peptide system that evolved before the origin of the CRF-calcitonin-secretin clade of peptides and plays a fundamental role in the regulation of cell-to-cell energy homeostasis. Moreover, it may act as a phylogenetically older peptide system that evolved as a natural antagonist to the CRF-mediated stress response. Thus, TCAP's actions on the CNS may provide new insights into the development of peptide therapeutics for the treatment of CNS disorders.

Teneurin C-terminal associated peptides (TCAPs) are an evolutionarily ancient family of 40- to 41-residue bioactive peptides located on the extracellular end of each of the four teneurin transmembrane proteins. TCAP-1 may exist as a tethered peptide at the teneurin-1 carboxy end or as an independent peptide that is either released via post-transcriptional cleavage from its teneurin-1 pro-protein or independently expressed as its own mRNA. In neurons, soluble TCAP-1 acts as a paracrine factor to regulate cellular activity and neuroplastic interactions. In vitro studies indicate that, by itself, synthetic TCAP-1 promotes neuron growth and protects cells from chemical insult. In vivo, TCAP-1 increases hippocampal neuron spine density, reduces stress-induced behavior and ablates cocaine-seeking behaviors. Together, these studies suggest that the physiological effects of TCAP-1 are a result of an inhibition of corticotropin-releasing factor (CRF) activity leading to increased energy production. This hypothesis is supported by in vivo functional positron emissions tomography studies, which demonstrate that TCAP-1 significantly increases glucose uptake in rat brain. Complimentary in vitro studies show that enhanced glucose uptake is the result of TCAP-1-induced insertion of the glucose transporter into the neuronal plasma membrane, leading to increased glucose uptake and ATP production. Interestingly, TCAP-1-mediated glucose uptake occurs through a novel insulin-independent pathway. This review will focus on examining the role of TCAP on neuronal energy metabolism in the central nervous system.