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Abstract We revisit the long-studied radial velocity (RV) target HD 26965 using recent observations from the NASA-NSF “NEID” precision Doppler facility. Leveraging a suite of classical activity indicators, combined with line-by-line RV analyses, we demonstrate that the claimed 45-day signal previously identified as a planet candidate is most likely an activity-induced signal. Correlating the bulk (spectrally averaged) RV with canonical line activity indicators confirms a multiday “lag” between the observed activity indicator time series and the measured RV. When accounting for this lag, we show that much of the observed RV signal can be removed by a linear detrending of the data. Investigating activity at the line-by-line level, we find a depth-dependent correlation between individual line RVs and the bulk RVs, further indicative of periodic suppression of convective blueshift causing the observed RV variability, rather than an orbiting planet. We conclude that the combined evidence of the activity correlations and depth dependence is consistent with an RV signature dominated by a rotationally modulated activity signal at a period of ∼42 days. We hypothesize that this activity signature is due to a combination of spots and convective blueshift suppression. The tools applied in our analysis are broadly applicable to other stars and could help paint a more comprehensive picture of the manifestations of stellar activity in future Doppler RV surveys. 

First release of software for the article: Searching for Low-Mass Exoplanets Amid Stellar Variability with a Fixed Effects Linear Model of Line-by-Line Shape Changes. This repository includes code and summaries corresponding to the paper "Searching for Low-Mass Exoplanets Amid Stellar Variability with a Fixed Effects Linear Model of Line-by-Line Shape Changes". The code uses R (https://www.r-project.org/) and assumes you have installed the following packages: tidyverse, rhdf5, Matrix, patchwork, collapse, parallel, pbmcapply Data used for this paper can be found at: https://doi.org/10.5281/zenodo.14841436 If you come across any issues or bugs, please contact Joseph Salzer at jsalzer@wisc.edu. 

The following repository contains the data used for the manuscript: "Searching for Low-Mass Exoplanets Amid Stellar Variability with a Fixed Effects Linear Model of Line-by-Line Shape Changes". Within line_property_files.zip there is file "line_property_files_README.md" that contains a description of each column of completeLines.csv. The important columns of this csv are date: time of observation line_order: unique identifier of a line, a combination of that line's central wavelength and order ID rv_template_0.5: the RV measure for a given line on a given day fit_gauss_a,fit_gauss_b,fit_gauss_depth,fit_gauss_sigmasq,proj_hg_coeff_0,proj_hg_coeff_2,proj_hg_coeff_3,proj_hg_coeff_4,proj_hg_coeff_5,proj_hg_coeff_6,proj_hg_coeff_7,proj_hg_coeff_8,proj_hg_coeff_9,proj_hg_coeff_10: the shape-change covariates that we use to control for stellar activity Below is a description of each of the data files completeLines.csv: this csv file contains the time series of every line's shape measurements and RVs, it is used throughout the analysis. line_property_files.zip: this directory contains .h5 files that contain all line-shape information and contaminated RVs for each line used in our analysis. The script clean_data.Rmd uses these as input and combines them all into a single csv file called completeLines.csv. project_template_deriv_onto_gh.h5: this contains the projection vector described in the paper to produce the orthogonal HG coefficients. The script clean_data.Rmd uses this as input and combines them all into a single csv file called completeLines.csv. models.zip: this directory contains the results from each model that was fit for our paper. 

Exoplanets can be detected with various observational techniques. Among them, radial velocity (RV) has the key advantages of revealing the architecture of planetary systems and measuring planetary mass and orbital eccentricities. RV observations are poised to play a key role in the detection and characterization of Earth twins. However, the detection of such small planets is not yet possible due to very complex, temporally correlated instrumental and astrophysical stochastic signals. Furthermore, exploring the large parameter space of RV models exhaustively and efficiently presents difficulties. In this review, we frame RV data analysis as a problem of detection and parameter estimation in unevenly sampled, multivariate time series. The objective of this review is two-fold: to introduce the motivation, methodological challenges, and numerical challenges of RV data analysis to nonspecialists, and to unify the existing advanced approaches in order to identify areas for improvement.