Data-driven Modeling of Telluric Features and Stellar Variability with StellarSpectraObservationFitting.jl
12 affiliations
- 1Center for Computing Research
- 2Penn State University
- 3Penn State Astrobiology Research Center
- 4State University
- 5Jet Propulsion Laboratory
- 6University of Pennsylvania
- 7Astrophysics & Space Institute
- 8ETH Zurich
- 9The University of California
- 10Tata Institute of Fundamental Research
- 11Macquarie University
- 12Carleton College
Abstract
A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching submillisecond accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here, we present StellarSpectraObservationFitting.jl ( SSOF ), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line spread function. We demonstrate SSOF ’s state-of-the-art performance on data from the NEID spectrometer on the WIYN 3.5 m Telescope for multiple stars. We show SSOF ’s ability to accurately identify and characterize spectral variability, provide ∼2–6× smaller photon-limited errors over the NEID CCF-based pipeline, and match the performance of SERVAL , a leading template-based pipeline, using only observed EPRV spectra.
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BibTeX
@article{gilbertson2026datadriven,
title = {{Data-driven Modeling of Telluric Features and Stellar Variability with StellarSpectraObservationFitting.jl}},
author = {Christian Gilbertson and Eric B. Ford and Samuel Halverson and Evan Fitzmaurice and Cullen H. Blake and Gumundur Stefánsson and Suvrath Mahadevan and Jason T. Wright and Jacob K. Luhn and Joe P. Ninan and Paul Robertson and Arpita Roy and Christian Schwab and Ryan C. Terrien},
journal = {The Astronomical Journal},
year = {2026},
month = oct,
doi = {10.3847/1538-3881/aea69e},
url = {https://iopscience.iop.org/article/10.3847/1538-3881/aea69e},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Data-driven Modeling of Telluric Features and Stellar Variability with StellarSpectraObservationFitting.jl
AU - Christian Gilbertson
AU - Eric B. Ford
AU - Samuel Halverson
AU - Evan Fitzmaurice
AU - Cullen H. Blake
AU - Gumundur Stefánsson
AU - Suvrath Mahadevan
AU - Jason T. Wright
AU - Jacob K. Luhn
AU - Joe P. Ninan
AU - Paul Robertson
AU - Arpita Roy
AU - Christian Schwab
AU - Ryan C. Terrien
PY - 2026
DA - 2026/10/15
JO - The Astronomical Journal
DO - 10.3847/1538-3881/aea69e
UR - https://iopscience.iop.org/article/10.3847/1538-3881/aea69e
AB - A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching submillisecond accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here, we present StellarSpectraObservationFitting.jl ( SSOF ), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line spread function. We demonstrate SSOF ’s state-of-the-art performance on data from the NEID spectrometer on the WIYN 3.5 m Telescope for multiple stars. We show SSOF ’s ability to accurately identify and characterize spectral variability, provide ∼2–6× smaller photon-limited errors over the NEID CCF-based pipeline, and match the performance of SERVAL , a leading template-based pipeline, using only observed EPRV spectra.
SN - 0004-6256
SN - 1538-3881
ER -