Two-stage GMM clustering of close-in exoplanets in dynamical feature space mapped to pebble-accretion models identifies sub-populations with distinct formation histories including earlier epochs for very-massive gas giants.
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NGTS-39 b is a 1.467 MJ, 1.088 RJ transiting warm Jupiter on a 58.2-day eccentric orbit around an F9 dwarf, identified via TESS, NGTS, CORALIE and HARPS data.
CARMApy provides a Python interface to the ExoCARMA microphysics code, enabling simulation of cloud particle size distributions and rates in exoplanet atmospheres with claimed consistency to prior versions and speed gains of 1.9x single-threaded and 3.8x multithreaded.
An ensemble of machine learning models trained jointly on Kepler and TESS data provides instrument-agnostic prioritization of exoplanet candidates.
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.
citing papers explorer
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Machine-learning clustering of close-in exoplanet populations: links to pebble accretion
Two-stage GMM clustering of close-in exoplanets in dynamical feature space mapped to pebble-accretion models identifies sub-populations with distinct formation histories including earlier epochs for very-massive gas giants.
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NGTS-39 b: A 58 d transiting warm Jupiter in an eccentric orbit
NGTS-39 b is a 1.467 MJ, 1.088 RJ transiting warm Jupiter on a 58.2-day eccentric orbit around an F9 dwarf, identified via TESS, NGTS, CORALIE and HARPS data.
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CARMApy: An Open-Source Python Framework for Simulating Microphysical Clouds in Planetary Atmospheres
CARMApy provides a Python interface to the ExoCARMA microphysics code, enabling simulation of cloud particle size distributions and rates in exoplanet atmospheres with claimed consistency to prior versions and speed gains of 1.9x single-threaded and 3.8x multithreaded.
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Towards Instrument-Agnostic Exoplanet Candidate Prioritization
An ensemble of machine learning models trained jointly on Kepler and TESS data provides instrument-agnostic prioritization of exoplanet candidates.
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Machine Learning as a Transformative Tool for (Exo-)Planetary Science
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.