Improving Earth-like planet detection in radial velocity using deep learning

no code implementations • 21 May 2024 • Yinan Zhao, Xavier Dumusque, Michael Cretignier, Andrew Collier Cameron, David W. Latham, Mercedes López-Morales, Michel Mayor, Alessandro Sozzetti, Rosario Cosentino, Isidro Gómez-Vargas, Francesco Pepe, Stephane Udry

By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD128621 and HD10700, a detection threshold of 0. 5 m/s in semi-amplitude for planets with periods ranging from 10 to 300 days.

Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks

1 code implementation • 30 Oct 2020 • Zoe L. de Beurs, Andrew Vanderburg, Christopher J. Shallue, Xavier Dumusque, Andrew Collier Cameron, Christopher Leet, Lars A. Buchhave, Rosario Cosentino, Adriano Ghedina, Raphaëlle D. Haywood, Nicholas Langellier, David W. Latham, Mercedes López-Morales, Michel Mayor, Giusi Micela, Timothy W. Milbourne, Annelies Mortier, Emilio Molinari, Francesco Pepe, David F. Phillips, Matteo Pinamonti, Giampaolo Piotto, Ken Rice, Dimitar Sasselov, Alessandro Sozzetti, Stéphane Udry, Christopher A. Watson

We trained our machine learning models on both simulated data (generated with the SOAP 2. 0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019).

BIG-bench Machine Learning regression