Search Results for author:
Found 6 papers, 2 papers with code
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks
When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters.
Indications for very high metallicity and absence of methane for the eccentric exo-Saturn WASP-117b
Using a 1D atmosphere model with isothermal temperature, uniform cloud deck and equilibrium chemistry, the Bayesian evidence of a retrieval analysis of the transmission spectrum indicates a preference for a high atmospheric metallicity ${\rm [Fe/H]}=2. 58^{+0. 26}_{-0. 37}$ and clear skies.
Earth and Planetary Astrophysics Solar and Stellar Astrophysics
Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection.
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra.
Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3, 000, 000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator.
