Understanding stellar activity-induced radial velocity jitter using simultaneous <i>K2 </i>photometry and HARPS RV measurements
Bibliographic record
Abstract
One of the best ways to improve our understanding of the stellar activity-induced signal in radial velocity (RV) measurements is through simultaneous high-precision photometric and RV observations. This is of prime importance to mitigate the RV signal induced by stellar activity and therefore unveil the presence of low-mass exoplanets. The K2 Campaign 7 and 8 fields of view were located in the southern hemisphere, and provided a unique opportunity to gather unprecedented simultaneous high-precision photometric observation with K2 and high-precision RV measurements with the HARPS spectrograph to study the relationship between photometric variability and RV jitter. We observed nine stars with different levels of activity, from quiet to very active. We first probed the presence of any meaningful relation between measured RV jitter and the simultaneous photometric variation, and also other activity indicators (such as BIS, FWHM, log R′HK, and F8) by evaluating the strength and significance of the monotonic correlation between RVs and each indicator. We found that for the case of very active stars, strong and significant correlations exist between almost all the observables and measured RVs; however, when we move towards lower activity levels the correlations become random, and we could not reach any conclusion regarding the tendency of correlations depending on the stellar activity level. Except for the F8 whose strong correlation with RV jitter persists over a wide range of stellar activity level, and thus our result suggests that F8 might be a powerful proxy for activity-induced RV jitter over a wide range of stellar activity. Moreover, we examine the capability of two state-of-the-art modeling techniques, namely the FF′ method and SOAP2.0, to accurately predict the RV jitter amplitude using the simultaneous photometric observation. We found that for the very active stars both techniques can predict the amplitude of the RV jitter reasonably well; however, at lower activity levels the FF′ method underpredicts the RV jitter amplitude.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".