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Active regions on the photosphere of a star have been the major obstacle fordetecting Earth-like exoplanets using the radial velocity (RV) method. Acommonly employed solution for addressing stellar activity is to assume alinear relationship between the RV observations and the activity indicatorsalong the entire time series, and then remove the estimated contribution ofactivity from the variation in RV data (overall correction method). However,since active regions evolve on the photosphere over time, correlations betweenthe RV observations and the activity indicators will correspondingly beanisotropic. We present an approach that recognizes the RV locations where thecorrelations between the RV and the activity indicators significantly change inorder to better account for variations in RV caused by stellar activity. Theproposed approach uses a general family of statistical breakpoint methods,often referred to as change point detection (CPD) algorithms; severalimplementations of which are available in R and python. A thorough comparisonis made between the breakpoint-based approach and the overall correctionmethod. To ensure wide representativity, we use measurements from real starsthat have different levels of stellar activity and whose spectra have differentsignal-to-noise ratios. When the corrections for stellar activity are appliedseparately to each temporal segment identified by the breakpoint method, thecorresponding residuals in the RV time series are typically much smaller thanthose obtained by the overall correction method. Consequently, the generalizedLomb-Scargle periodogram contains a smaller number of peaks caused by activeregions. The CPD algorithm is particularly effective when focusing on activestars with long time series, such as alpha Cen B.

Accounting for stellar activity signals in radial-velocity data by using Change Point Detection techniques