most citedCharacterizing Jupiter's interior using machine learning reveals four key structures

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

astro-ph.EP20247 cited

Characterizing Jupiter's interior using machine learning reveals four key structures

Maayan Ziv, Eli Galanti, Saburo Howard +2

The internal structure of Jupiter is constrained by the precise gravity field measurements by NASA's Juno mission, atmospheric data from the Galileo entry probe, and Voyager radio…

astro-ph.EP2024

NeuralCMS: A deep learning approach to study Jupiter's interior

Maayan Ziv, Eli Galanti, Amir Sheffer +3

NASA's Juno mission provided exquisite measurements of Jupiter's gravity field that together with the Galileo entry probe atmospheric measurements constrains the interior structure…

astro-ph.EP2023

Three-dimensional atmospheric dynamics of Jupiter from ground-based Doppler imaging spectroscopy in the visible

François-Xavier Schmider, Patrick Gaulme, Raúl Morales-Juberías +25

We present three-dimensional (3D) maps of Jupiter's atmospheric circulation at cloud-top level from Doppler-imaging data obtained in the visible domain with JIVE, the second node o…

astro-ph.EP2023

On the hypothesis of an inverted Z-gradient inside Jupiter

Saburo Howard, Tristan Guillot, Steve Markham +6

Models of Jupiter s interior struggle to agree with measurements of the atmospheric composition. Interior models favour a subsolar or solar abundance of heavy elements Z while atmo…

astro-ph.EP2023

TOI-199 b: A well-characterized 100-day transiting warm giant planet with TTVs seen from Antarctica

Melissa J. Hobson, Trifon Trifonov, Thomas Henning +49

We present the spectroscopic confirmation and precise mass measurement of the warm giant planet TOI-199 b. This planet was first identified in TESS photometry and confirmed using g…