Exoplanets

Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stellar jitter. In this work, we develop a machine learning (ML) framework based on a transformer architecture that aims to detect the presence and likely period of planetary signals in time-series spectra, even in the presence of stellar activity. The model is trained to classify whether a planetary signal exists and assign it to one of several discrete period and amplitude bins. Injection-recovery tests on randomly selected 100 epoch observation subsets from NEID solar data (2020─2022 period) show that for low-amplitude systems (\<1 m s−1), our model improves planetary candidate identification by a factor of 2 compared to the traditional Lomb─Scargle periodogram. Our ML model is built on a Vision Transformer architecture that processes reduced representations of solar spectrum observations to predict the period and semi-amplitude of planetary signal candidates. By analyzing multiepoch spectra, the model reliably detects planetary signals with semi-amplitudes as low as 65 cm s−1. Even under real solar noise and irregular sampling, it identifies signals down to 35 cm s−1. Comparisons with the Lomb─Scargle periodogram demonstrate a significant improvement in detecting low-amplitude planetary candidates, particularly for longer orbital periods. These results underscore the potential of ML to identify planetary candidates early in EPRV surveys, even from limited observational counts.