05: Searching for Exoplanets in Stellar Spectra: Embedding Techniques for Local Feature Shape Analysis

Music City Center 

Detecting Earth-like exoplanets presents a significant statistical challenge due to their weak signals, which can be obscured or even mimicked by stellar activity (e.g., sunspots, faculae). In this work, we propose a statistical approach to enhance exoplanet detection by analyzing a time series of stellar spectra while accounting for the confounding effects of stellar activity. We model the stellar spectra as a functional time series, using local spectral features to estimate planetary signals. However, stellar activity distorts the shape of these local features over time, introducing variability that can interfere with planetary detection. To address this, we apply dissimilarity metrics and dimension reduction techniques to characterize shape changes in the local features; the resulting embeddings are then incorporated into a statistical model to produce a clearer exoplanet signal. We leverage data from hundreds of the local spectral features to disentangle the effects of stellar activity from true planetary signals.

astrostatistics

dimension reduction

time-series analysis

statistical shape analysis 

Astrostatistics Interest Group