Transit photometry & RV
Exoplanets
Detecting planetary candidates and characterizing them from transit light curves and radial-velocity signals.
When a planet passes in front of its host star, the resulting photometric dip — a transit light curve — encodes the planet's radius, orbital inclination, and the limb-darkening profile of the star. With enough precision, repeated transits also reveal atmospheric absorption signatures, orbital decay, and transit-timing variations caused by companion planets.
We develop ML-assisted pipelines for radial-velocity analysis and transit detrending that reduce sensitivity to stellar activity — the dominant noise source in exoplanet detection. Our 2026 paper introduced a vision-transformer model for radial-velocity analysis that improves planet recovery in low signal-to-noise regimes.
Related publications
See all in Exoplanets →Machine Learning for Radial Velocity Analysis. I. Vision Transformers as a Robust Alternative for Detecting Planetary Candidates
Anoop Gavankar, Tanish Mittal, Joe Ninan, Shravan Hanasoge
Team members
- Anoop GavankarPhD Student
Collaborators
- Google DeepMindComputing
- Google DeepMindInternational
For all peer-reviewed publications across the group, see the full publications page.
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