Related papers: Martian Ionosphere Electron Density Prediction Usi…
The impact of a rarest solar wind phenomenon [disappearing solar wind (DSW) event during 26-28 December 2022] on the Martian nightside ionosphere is investigated, for the first time, using MAVEN datasets. During an extremely low solar wind…
Airborne dust is the main climatic agent in the Martian environment. Local dust storms play a key role in the dust cycle; yet their life cycle is poorly known. Here we use mesoscale modeling that includes the transport of radiatively active…
We develop multiple Deep Learning (DL) models that advance the state-of-the-art predictions of the global auroral particle precipitation. We use observations from low Earth orbiting spacecraft of the electron energy flux to develop a model…
The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and…
Mars, lacking an intrinsic dynamo, is an ideal laboratory to comparatively study induced magnetospheres, which can be found in other terrestrial bodies as well as comets. Additionally, Mars is of particular interest to further exploration…
This article presents a monitoring of the atmospheric dust in the south polar region during spring of martian year 27. Our goal is to contribute to identifying the regions where the dust concentration in the atmosphere shows specific…
The debate over the early Martian climate is among the most intriguing in planetary science. Although the geologic evidence generally supports a warmer and wetter climate, climate models have had difficulty simulating such a scenario,…
Thermal tides in the Martian atmosphere are analyzed using temperature profiles retrieved from nadir observations obtained by the TIRVIM Fourier-spectrometer, part of the Atmospheric Chemistry Suite (ACS) onboard the ExoMars Trace Gas…
The Martian brain terrain (MBT), characterized by its unique brain-like morphology, is a potential geological archive for finding hints of paleoclimatic conditions during its formation period. The morphological similarity of MBT to…
I discuss the effects of measurement error on regression and density estimation. I review the statistical methods that have been developed to correct for measurement error that are most popular in astronomical data analysis, discussing…
The ionosphere has been normally detected by traditional instruments, such as ionosonde, scatter radars, topside sounders onboard satellites and in situ rocket. However, most instruments are expensive and also restricted to either the…
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, decision tree and deep learning approaches were developed to…
Severe Ionosphere magnetic storms are feared events for integrity and continuity of navigation systems such as EGNOS, the European SBAS (Satellite-Based Augmentation System) complementing GPS and an accurate modelling of this event…
Present-day Mars is cold and dry, but mineralogical and morphological evidence shows that liquid-water existed on the surface of ancient Mars. In order to explain this evidence and assess ancient Mars's habitability, one must understand the…
Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric…
Standard Bayesian retrievals for exoplanet atmospheric parameters from transmission spectroscopy, while well understood and widely used, are generally computationally expensive. In the era of the JWST and other upcoming observatories,…
High resolution geospatial data are challenging because standard geostatistical models based on Gaussian processes are known to not scale to large data sizes. While progress has been made towards methods that can be computed more…
We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines…
We have produced a multiannual climatology of airborne dust from Martian year 24 to 31 using multiple datasets of retrieved or estimated column optical depths. The datasets are based on observations of the Martian atmosphere from April 1999…
We present a model of radiative transfer through atmospheric particles based on Monte Carlo methods. This model can be used to analyze and remove the contribution of aerosols in remote sensing observations. We have developed a method to…