Machine Learning for Planet Detection in the Presence of Stellar Variability: Applications to Solar EPRV Observations and TESS Light Curves

Planetary signal detection in stellar time-series observations is often limited by stellar variability, instrumental noise, and irregular sampling. Here, we present two related machine-learning approaches designed to recover weak planetary signals in two observational settings: solar extreme precision radial velocity (EPRV) observations and space-based photometric light curves. 

The first approach focuses on recovering low-amplitude injected planetary signals in solar EPRV data. Periodograms remain a standard tool for identifying periodic signals, but their performance can be limited when weak planetary signals are embedded in activity-driven stellar radial-velocity variations. We develop a model based on a Vision Transformer architecture that uses a reduced representation of spectroscopic time-series observations to predict the period and semi-amplitude of the injected signal. In injection-recovery tests using randomly selected 100-observation subsets from NEID solar data collected between 2020 and 2022, we find that the model improves signal recovery by about a factor of two over the Lomb-Scargle periodogram for systems with semi-amplitudes below 1 m/s. 

The second approach applies a related data-driven framework to transit detection and period estimation in TESS light curves. We use a Conv-Transformer architecture in which convolutional layers capture short transit-like dips, while transformer modules model longer temporal structure. The model is trained and validated using transit injections into TESS light curves, with additional tests on known TESS Objects of Interest. Injection-recovery experiments, including main-sequence stars and cool dwarfs, show consistent recovery of injected signals and stable period estimates under realistic noise conditions. Current work extends this framework to younger and more active stellar populations, including young stellar objects, where stellar variability can obscure or mimic transit signals. 

Together, these projects examine how deep-learning models can improve the recovery and characterization of weak planetary signals in spectroscopic and photometric time series. Overall, these results suggest that machine-learning methods can help identify promising periodic signals more efficiently in large astronomical data sets, supporting future searches for small planet candidates and other transit-like systems that require follow-up validation and classification.