Related papers: Grid-based exoplanet atmospheric mass loss predict…
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the…
We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine learning technique, to overcome…
After training complex deep learning models, a common task is to compress the model to reduce compute and storage demands. When compressing, it is desirable to preserve the original model's per-example decisions (e.g., to go beyond top-1…
The characterization of an exoplanet's interior is an inverse problem, which requires statistical methods such as Bayesian inference in order to be solved. Current methods employ Markov Chain Monte Carlo (MCMC) sampling to infer the…
Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed…
We present an analytic model for the interaction between planetary atmospheres and stellar winds from main sequence M stars, with the purpose of obtaining a quick test-model that estimates the timescale for total atmospheric mass loss due…
The multigrid algorithm is a multilevel approach to accelerate the numerical solution of discretized differential equations in physical problems involving long-range interactions. Multiresolution analysis of wavelet theory provides an…
Massive stars play a fundamental role in galactic evolution through their strong stellar winds, chemical enrichment, and feedback mechanisms. Accurate modelling of their atmospheres and winds is critical for understanding their physical…
The classical picture of our Solar System being the archetypal outcome of planet formation has been rendered obsolete by the astonishing diversity of extrasolar-system architectures. From rare hot-Jupiters to abundant super-Earths and…
Giant exoplanets located >1 AU away from their parent stars have atmospheric environments cold enough for water and/or ammonia clouds. We have developed a new equilibrium cloud and reflected light spectrum model, ExoREL, for widely…
Evolutionary synthesis models are a fundamental tool to interpret the properties of observed stellar systems. In order to achieve a meaningful comparison between models and real data, it is necessary to calibrate the models themselves, i.e.…
Modeling the outflow of planetary atmospheres is important for understanding the evolution of exoplanet systems and for interpreting their observations. Modern theoretical models of exoplanet atmospheres become increasingly detailed and…
Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying…
We present a machine learning (ML) approach for the prediction of galaxies' dark matter halo masses that achieves an improved performance over conventional methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and neural…
Static structure models, which map mass-radius constraints to bulk planet composition, are frequently used to categorise exoplanets due to their computational efficiency and the high-level insight they offer into planetary properties.…
We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of many-body systems. Scalability and transferability are central to the unprecedented computational efficiency of…
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain…
This is the second paper in a series where we build a self-consistent model to simulate the mass-loss process of a close-orbit magnetized giant exoplanet, so-called hot Jupiter (HJ). In this paper we generalize the hydrodynamic (HD) model…
A space-based gravitational microlensing exoplanet survey will provide a statistical census of exoplanets with masses down to 0.1 Earth-masses and orbital separations ranging from 0.5AU to infinity. This includes analogs to all the Solar…
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce errors and increase the resolution of ESMs. However,…