Tootfinder

Detecting False Positives With Derived Planetary Parameters: Experimenting with the KEPLER Dataset
Ayan Bin Rafaih, Zachary Murray
https://arxiv.org/abs/2508.13801 https://

Detecting False Positives With Derived Planetary Parameters: Experimenting with the KEPLER Dataset
Recent developments in computational power and machine learning techniques motivate their use in many different astrophysical research areas. Consequently, many machine learning models have been trained to classify exoplanet transit signals - typically done by using time series light curves. In this work, we attempt a different approach and try to improve the efficiency of these algorithms by fitting only derived planetary parameters, instead of full time-series light curves. We investigate and…

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
https://arxiv.org/abs/2510.07034

Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates
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…