Brandt, G. Mirek | Search NASA

DOE OSTI2021

Precise Dynamical Masses and Orbital Fits for β Pic b and β Pic c

We present a comprehensive orbital analysis to the exoplanets β Pictoris b and c that resolves previously reported tensions between the dynamical and evolutionary mass constraints on β Pic b. We use the Markov Chain Monte Carlo orbit code orvara to fit 15 years of radial velocities and relative astrometry (including recent GRAVITY measurements), absolute astrometry from Hipparcos and Gaia, and a single relative radial velocity measurement between β Pic A and b. We measure model-independent masses of 9.3{sub −2.5}{sup +2.6} M {sub Jup} for β Pic b and 8.3 ± 1.0 M {sub Jup} for β Pic c. These masses are robust to modest changes to the input data selection. We find a well-constrained eccentricity of 0.119 ± 0.008 for β Pic b, and an eccentricity of 0.21{sub −0.09}{sup +0.16} for β Pic c, with the two orbital planes aligned to within ∼05. Both planets’ masses are within ∼1σ of the predictions of hot-start evolutionary models and exclude cold starts. We validate our approach on N-body synthetic data integrated using REBOUND. We show that orvara can account for three-body effects in the β Pic system down to a level ∼5 times smaller than the GRAVITY uncertainties. Systematics in the masses and orbital parameters from orvara’s approximate treatment of multiplanet orbits are a factor of ∼5 smaller than the uncertainties we derive here. Future GRAVITY observations will improve the constraints on β Pic c’s mass and (especially) eccentricity, but improved constraints on the mass of β Pic b will likely require years of additional radial velocity monitoring and improved precision from future Gaia data releases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Automatic Échelle Spectrograph Wavelength Calibration

Time-domain astronomy and the increasing number of exoplanet candidates call for reliable, robust, and automatic wavelength calibration. We present an algorithm for wavelength-calibrating échelle spectrographs that uses order-by-order extracted spectra and a list of laboratory wavelengths. Our approach is fully automatic and does not need the pixel locations of certain spectral features with which to anchor the wavelength solution, nor the true order number of each diffraction order. We use spectral features that are duplicated in adjacent orders to establish the scale-invariant component of the wavelength solution. We then match the central wavelengths of spectral features to laboratory wavelengths to establish the scale and higher-order components of the wavelength solution. We demonstrate our method on the four spectrographs of Las Cumbres Observatory’s Network of Robotic Échelle Spectrographs (NRES), on the High Accuracy Radial Velocity Planet Searcher (HARPS) spectrograph, and on synthetic data. We obtain a velocity-equivalent precision of ∼10 m s{sup −1} on NRES. We achieve ∼1 m s{sup −1} on HARPS, which agrees with the precision reported by the HARPS team. On synthetic data, we achieve the velocity precision set by Gaussian centroiding errors. Our algorithm likely holds for a wide range of spectrographs beyond the five presented here. We provide an open-source Python package, xwavecal, which outputs wavelength-calibrated spectra as well as the wavelengths of spectral features.

79 ASTRONOMY AND ASTROPHYSICS↗