arXiv Machine Learning

Machine-learning clustering of close-in exoplanet populations: links to pebble accretion

arXiv:2606. 11737v1 Announce Type: cross Abstract: Close-in exoplanets exhibit a wide range of orbital architectures and physical properties shaped by both formation conditions and migration processes.

arXiv Machine Learning
Jun 26

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510. 17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved.

By Bo Liang, Hanlin Song, Chang Liu, Tianyu Zhao, Yuxiang Xu, Zihao Xiao, Manjia Liang, Minghui Du, Wei-Liang Qian, Li-e Qiang, Peng Xu, Ziren Luo
arXiv Machine Learning
Jun 24

An Agnostic Machine Learning Model of Photosynthetic Habitability

arXiv:2606. 24458v1 Announce Type: cross Abstract: The search for exoplanet biosignatures is guided by whether planetary environments can sustain photosynthesis.

By Callum Gray, Cassandra Hall, Stefano Santabarbara, Klaus Schmidt-Rohr, Andrew Ringham, Edward Gillen, Thomas J. Haworth, Christopher D. P. Duffy
arXiv Machine Learning
Jun 24

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.

By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga