Mitigating stellar activity jitter with different line lists for least-squares deconvolution: analysis of a parametric and a randomised line selection
Stefano Bellotti, Pascal Petit, Julien Morin, Gaitee Hussain, Colin, Folsom, Andres Carmona, Xavier Delfosse, Claire Moutou

TL;DR
This study compares line list selection methods for least-squares deconvolution to reduce stellar activity jitter in radial velocity data, finding that a randomized approach significantly improves jitter mitigation and aids in exoplanet detection around active M dwarfs.
Contribution
It introduces a randomized line selection algorithm that outperforms parametric methods in reducing stellar activity jitter in RV measurements of M dwarfs.
Findings
Randomized line selection reduces RV jitter by over 50%.
Parametric line selection reduces jitter by less than 10%.
The method enables detection of synthetic planets despite stellar activity.
Abstract
Stellar activity limits the radial velocity search and characterisation of exoplanets, as it introduces spurious jitter in the data sets and prevents the correct retrieval of a planetary signal. This is key for M dwarfs, considering that they manifest high activity levels and are primary targets for present and future searches of habitable Earth-like planets. Effective filtering of activity is crucial to achieve the sensitivity required for small planet detections. Here, we analyse the impact of selecting different line lists for least-squares deconvolution on the dispersion in our RV data sets, to identify the line list that most effectively reduces the jitter. We employ optical observations of the active M dwarf EV Lac collected with ESPaDOnS and NARVAL, and study two line selection approaches: a parametric one based on line properties and a randomised algorithm that samples the line…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Astronomical Observations and Instrumentation
