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Abstract Ground-based high-resolution cross-correlation spectroscopy (HRCCS;R ≳ 15,000) is a powerful complement to space-based studies of exoplanet atmospheres. By resolving individual spectral lines, HRCCS can precisely measure chemical abundance ratios, directly constrain atmospheric dynamics, and robustly probe multidimensional physics. But the subtleties of HRCCS data sets—e.g., the lack of exoplanetary spectra visible by eye and the statistically complex process of telluric removal—can make interpreting them difficult. In this work, we seek to clarify the uncertainty budget of HRCCS with a forward-modeling approach. We present an HRCCS observation simulator,scope,⁵⁵https://github.com/arjunsavel/scopethat incorporates spectral contributions from the exoplanet, star, tellurics, and instrument. This tool allows us to control the underlying data set, enabling controlled experimentation with complex HRCCS methods. Simulating a fiducial hot Jupiter data set (WASP-77Ab emission with IGRINS), we first confirm via multiple tests that the commonly used principal component analysis does not bias the planetary signal when few components are used. Furthermore, we demonstrate that mildly varying tellurics and moderate wavelength solution errors induce only mild decreases in HRCCS detection significance. However, limiting-case, strongly varying tellurics can bias the retrieved velocities and gas abundances. Additionally, in the low signal-to-noise ratio limit, constraints on gas abundances become highly non-Gaussian. Our investigation of the uncertainties and potential biases inherent in HRCCS data analysis enables greater confidence in scientific results from this maturing method. 

Abstract The element abundances of stars, particularly the refractory elements (e.g., Fe, Si, and Mg), play an important role in connecting stars to their planets. Most Sun-like stars do not have refractory abundance measurements since obtaining a large sample of high-resolution spectra is difficult with oversubscribed observing resources. In this work we infer abundances for C, N, O, Na, Mn, Cr, Si, Fe, Ni, Mg, V, Ca, Ti, Al, and Y for solar analogs with Gaia Radial Velocity Spectrometer (RVS) spectra (R= 11,200) usingTheCannon, a data-driven method. We train a linear model on a reference set of 34 stars observed by Gaia RVS with precise abundances measured from previous high-resolution spectroscopic efforts (R> 30,000–110,000). We then apply this model to several thousand Gaia RVS solar analogs. This yields abundances with average upper limit precisions of 0.04–0.1 dex for 17,412 stars, 50 of which are identified planet (candidate) hosts. We subsequently test the relative refractory depletion of these stars with increasing element condensation temperature compared to the Sun. The Sun remains refractory depleted compared to other Sun-like stars regardless of our current knowledge of the planets they host. This is inconsistent with theories of various types of planets locking up or sequestering refractories. Furthermore, we find no significant abundance differences between identified close-in giant planet hosts, giant planet hosts, and terrestrial/small planet hosts with the rest of the sample within our precision limits. This work demonstrates the utility of data-driven learning for future exoplanet composition and demographics studies. 

Abstract Pulsar distances are notoriously difficult to measure, and play an important role in many fundamental physics experiments, such as pulsar timing arrays. Here, we perform a cross-match between International PTA pulsars (IPTA) and Gaia's Data Release 2 (DR2) and Data Release 3 (DR3). We then combine the IPTA pulsar’s parallax with its binary companion’s parallax, found in Gaia, to improve the distance measurement to the binary. We find seven cross-matched IPTA pulsars in Gaia DR2, and when using Gaia DR3 we find six IPTA pulsar cross-matches but with seven Gaia objects. Moving from Gaia DR2 to Gaia DR3, we find that the Gaia parallaxes for the successfully cross-matched pulsars improved by 53%, and pulsar distances improved by 29%. Finally, we find that binary companions with a <3.0σdetection are unreliable associations, setting a high bar for successful cross-matches. 

Precise Gaia measurements of positions, parallaxes, and proper motions provide an opportunity to calculate 3D positions and 2D velocities (i.e., 5D phase-space) of Milky Way stars. Where available, spectroscopic radial velocity (RV) measurements provide full 6D phase-space information, however there are now and will remain many stars without RV measurements. Without an RV it is not possible to directly calculate 3D stellar velocities; however, one can infer 3D stellar velocities by marginalizing over the missing RV dimension. In this paper, we infer the 3D velocities of stars in the Kepler field in Cartesian Galactocentric coordinates (vx, vy, vz). We directly calculate velocities for around a quarter of all Kepler targets, using RV measurements available from the Gaia, LAMOST, and APOGEE spectroscopic surveys. Using the velocity distributions of these stars as our prior, we infer velocities for the remaining three quarters of the sample by marginalizing over the RV dimension. The median uncertainties on our inferred vx, vy, and vz velocities are around 4, 18, and 4 km/s, respectively. We provide 3D velocities for a total of 148,590 stars in the Kepler field. These 3D velocities could enable kinematic age-dating, Milky Way stellar population studies, and other scientific studies using the benchmark sample of well-studied Kepler stars. Although the methodology used here is broadly applicable to targets across the sky, our prior is specifically constructed from and for the Kepler field. Care should be taken to use a suitable prior when extending this method to other parts of the Galaxy. 

Abstract We present an analysis of Sun-as-a-star observations from four different high-resolution, stabilized spectrographs—HARPS, HARPS-N, EXPRES, and NEID. With simultaneous observations of the Sun from four different instruments, we are able to gain insight into the radial velocity precision and accuracy delivered by each of these instruments and isolate instrumental systematics that differ from true astrophysical signals. With solar observations, we can completely characterize the expected Doppler shift contributed by orbiting Solar System bodies and remove them. This results in a data set with measured velocity variations that purely trace flows on the solar surface. Direct comparisons of the radial velocities measured by each instrument show remarkable agreement with residual intraday scatter of only 15–30 cm s⁻¹. This shows that current ultra-stabilized instruments have broken through to a new level of measurement precision that reveals stellar variability with high fidelity and detail. We end by discussing how radial velocities from different instruments can be combined to provide powerful leverage for testing techniques to mitigate stellar signals. 

Abstract Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme-precision radial-velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The Extreme-precision Spectrograph (EXPRES) Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed radial-velocity (RV) correction, or separated out shape- and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV rms than classic linear decorrelation, but no method is yet consistently reducing the RV rms to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets—such as solar data or data with known injected planetary and/or stellar signals—to better understand method performance and whether planetary signals are preserved.