Characterizing and Mitigating the Impact of Telluric Absorption in Precise Radial Velocities
Bibliographic record
Abstract
Abstract Precise radial velocity (PRV) surveys are important for the search for Earth analogs around nearby bright stars, which induce a small stellar reflex motion with an RV amplitude of ∼10 cm s −1 . Detecting such a small RV signal poses challenges to instrumentation, data analysis, and the precision of astrophysical models to mitigate stellar jitter. In this work, we investigate an important component in the PRV error budget—the spectral contamination from the Earth’s atmosphere (tellurics). We characterize the effects of telluric absorption on the RV precision and quantify its contribution to the RV error budget over time and across a wavelength range of 350 nm–2.5 μ m. We use simulated solar spectra with telluric contamination injected, and we extract the RVs using two commonly adopted algorithms: dividing out a telluric model before performing cross-correlation or forward modeling the observed spectrum incorporating a telluric model. We assume various degrees of cleanness in removing the tellurics. We conclude that the RV errors caused by telluric absorption can be suppressed to close to or even below 1–10 cm s −1 in the blue optical region. At red through near-infrared wavelengths, however, the residuals of tellurics can induce an RV error on the meter-per-second level even under the most favorable assumptions for telluric removal, leading to significant systematic noise in the RV time series and periodograms. If the red-optical or near-infrared becomes critical in the mitigation of stellar activity, systematic errors from tellurics can be eliminated with a space mission such as EarthFinder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".