A Novel Statistical Framework for Recovering Radial Velocities from Line-by-Line Spectral Data

Thomas M. Menino Convention & Exhibition Center 

Detecting and characterizing Earth-analogue exoplanets with radial velocity (RV) measurements is a primary goal of modern astronomy. However, stellar variability can mimic or obscure the subtle RV signals caused by these low-mass planets. Changes in the shapes of spectral lines provide valuable information for disentangling this variability from true RV variation. In this work, we introduce a novel framework for analyzing spectroscopic time-series data, framing the spectra as high-dimensional panels of spectral lines measured repeatedly over time. Our model jointly estimates a shared temporal RV component and line-specific regression coefficients linking RV deviations to multiple shape descriptors extracted from hundreds of spectral lines. While the underlying statistical architecture is grounded in well-established methods, its power lies in the exploitation of estimated line-by-line shape information, producing significant reductions in RV root-mean-square error. When applied to a large spectroscopic dataset with known ground-truth RV signals, the model reduces the root-mean-square error by approximately 76% relative to uncorrected line-by-line Doppler shifts.

astrostatistics

exoplanet detection methods

astronomy data analysis

time series analysis