Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date are yet to be fully exploited. We employ The Cannon, a data-driven approach for estimating chemical abundances, to obtain detailed abundances from lowresolution (R=1800) LAMOST spectra, using the GALAH survey as our reference. We deliver five (for dwarfs) or six (for giants) estimated abundances representing five different nucleosynthetic channels, for 3.9 million stars, to a precision of 0.05-0.23 dex. Using wide binary pairs, we demonstrate that our abundance estimates provide chemical discriminating power beyond metallicity alone. We show the coverage of our catalog with radial, azimuthal and dynamical abundance maps and examine the neutron capture abundances across the disk and halo, which indicate different origins for the in situ and accreted halo populations. LAMOST has near-complete Gaia coverage and provides an unprecedented perspective on chemistry across the Milky Way.

Stellar abundances and ages afford the means to link chemical enrichment to galactic formation. In the Milky Way, individual element abundances show tight correlations with age, which vary in slope across ([Fe/H]–[α/Fe]). Here, we step from characterizing abundances as measures of age, to understanding how abundances trace properties of stellar birth environment in the disk over time. Using measurements from ∼27,000 APOGEE stars (R = 22,500, signal-to-noise ratio > 200), we build simple local linear models to predict a sample of elements (X = Si, O, Ca, Ti, Ni, Al, Mn, Cr) using (Fe, Mg) abundances alone, as fiducial tracers of supernovae production channels. Given [Fe/H] and [Mg/H], we predict these elements, [X/H], to about double the uncertainty of their measurements. The intrinsic dispersion, after subtracting measurement errors in quadrature is ≈0.015–0.04 dex. The residuals of the prediction (measurement − model) for each element demonstrate that each element has an individual link to birth properties at fixed (Fe, Mg). Residuals from primarily massive-star supernovae (i.e., Si, O, Al) partially correlate with guiding radius. Residuals from primarily supernovae Ia (i.e., Mn, Ni) partially correlate with age. A fraction of the intrinsic scatter that persists at fixed (Fe, Mg), however, after accounting for correlations, does not appear to further discriminate between birth properties that can be traced with present-day measurements. Presumably, this is because the residuals are also, in part, a measure of the typical (in)-homogeneity of the disk’s stellar birth environments, previously inferred only using open cluster systems. Our study implies at fixed birth radius and time that there is a median scatter of ≈0.01–0.015 dex in elements generated in supernovae sources.

By design, model-based approaches for flagging transiting exoplanets in light curves, such as boxed least squares, excel at detecting planets with low S/N at the expense of finding signals that are not well described by the assumed model, such as self-lensing binaries, disintegrating or evaporating planets, or planets with large rings. So far, such signals have typically been found through visual searches by professional or citizen scientists, or by inspection of the photometric power-spectra. We present a nonparametric detection algorithm, for short duty-cycle periodic signals in photometric time series based on phase dispersion minimization. We apply our code to 161,786 Kepler sources and detect 18 new periodic signals consistent with heartbeat binaries/planets, 4 new singly-transiting systems, and 2 new doubly-transiting systems. We show that our code is able to recover the majority of known Kepler objects of interest (KOIs) to high confidence, as well as more unusual events such as Boyajian's star and a comet passing through the Kepler field. Nonparametric signal-flagging techniques, such as the one presented here, will become increasingly valuable with the coming data from TESS and future transit surveys as the volume of data available to us exceeds that which can be feasibly examined manually.

Stellar light curves are well known to encode physical stellar properties. Precise, automated, and computationally inexpensive methods to derive physical parameters from light curves are needed to cope with the large influx of these data from space-based missions such as Kepler and TESS. Here we present a new methodology that we call “The Swan,” a fast, generalizable, and effective approach for deriving stellar surface gravity ( ) for main-sequence, subgiant, and red giant stars from Kepler light curves using local linear regression on the full frequency content of Kepler long-cadence power spectra. With this inexpensive data-driven approach, we recover to a precision of ∼0.02 dex for 13,822 stars with seismic values between 0.2 and 4.4 dex and ∼0.11 dex for 4646 stars with Gaia-derived values between 2.3 and 4.6 dex. We further develop a signal-to-noise metric and find that granulation is difficult to detect in many cool main-sequence stars (T eff ≲ 5500 K), in particular K dwarfs. By combining our measurements with Gaia radii, we derive empirical masses for 4646 subgiant and main-sequence stars with a median precision of ∼7%. Finally, we demonstrate that our method can be used to recover to a similar mean absolute deviation precision for a TESS baseline of 27 days. Our methodology can be readily applied to photometric time series observations to infer stellar surface gravities to high precision across evolutionary states.

We present Korg, a new package for 1D LTE spectral synthesis of FGK stars, which computes theoretical spectra from the near-ultraviolet to the near-infrared, and implements both plane-parallel and spherical radiative transfer. We outline the inputs and internals of Korg, and compare synthetic spectra from Korg, Moog, Turbospectrum, and SME. The disagreements between Korg and the other codes are no larger than those between the other codes, although disagreement between codes is substantial. We examine the case of a C2 band in detail, finding that uncertainties on physical inputs to spectral synthesis account for a significant fraction of the disagreement. Korg is 1–100 times faster than other codes in typical use, compatible with automatic differentiation libraries, and easily extensible, making it ideal for statistical inference and parameter estimation applied to large data sets. Documentation and installation instructions are available at https://ajwheeler.github.io/Korg.jl/stable/.