Related papers: Grid-based exoplanet atmospheric mass loss predict…
Planet formation simulations are capable of directly integrating the evolution of hundreds to thousands of planetary embryos and planetesimals, as they accrete pairwise to become planets. In principle such investigations allow us to better…
Modeling of turbulent flows is still challenging. One way to deal with the large scale separation due to turbulence is to simulate only the large scales and model the unresolved contributions as done in large-eddy simulation (LES). This…
Ground-based solar image restoration is a computationally expensive procedure that involves nonlinear optimization techniques. The presence of atmospheric turbulence produces perturbations in individual images that make it necessary to…
Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in a data-driven manner. Several works have attempted to…
In the present series of papers we propose a consistent description of the mass loss process. To study the effects of intrinsic magnetic field of a close-orbit giant exoplanet (so-called Hot Jupiter) on the atmospheric material escape and…
Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study…
With over 1800 planets discovered outside of the Solar System in the past two decades, the field of exoplanetology has broadened our perspective on planetary systems. Research priorities are now moving from planet detection to planet…
Reliable predictions of mass-loss rates are important for massive-star evolution computations. We aim to provide predictions for mass-loss rates and wind-momentum rates of O-type stars, carefully studying the behaviour of these winds as…
The study of extrasolar planets has rapidly expanded to encompass the search for new planets, measurements of sizes and masses, models of planetary interiors, planetary demographics and occurrence frequencies, the characterization of…
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are…
We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of…
Various methods have been proposed to search for extrasolar planets. Compared to the other methods, microlensing has unique applicabilities to the detections of Earth-mass and free-floating planets. However, the microlensing method is…
Studying neural network loss landscapes provides insights into the nature of the underlying optimization problems. Unfortunately, loss landscapes are notoriously difficult to visualize in a human-comprehensible fashion. One common way to…
Atmospheric correction is a fundamental task in remote sensing because observations are taken either of the atmosphere or looking through the atmosphere. Atmospheric correction errors can significantly alter the spectral signature of the…
Mass and radius are two fundamental properties for characterising exoplanets, but only for a relatively small fraction of exoplanets are they both available. Mass is often derived from radial velocity measurements, while the radius is…
This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the…
One of the long-term goals of exoplanet science is the (atmospheric) characterization of a large sample (>100) of terrestrial planets to assess their potential habitability and overall diversity. Hence, it is crucial to quantitatively…
In recent years, machine learning (ML) algorithms have been successfully employed in Astronomy for analyzing and interpreting the data collected from various surveys. The need for new robust and efficient data analysis tools in Astronomy is…
Many evolutionary models of giant exoplanets still rely on simplifying assumptions that are no longer adequate given detailed constraints from Jupiter, Saturn, and modern exoplanet observations. Here, we identify the key physical…
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only…