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The fast and accurate estimation of planetary mass-loss rates is critical for planet population and evolution modelling. We use machine learning (ML) for fast interpolation across an existing large grid of hydrodynamic upper atmosphere…
We address the challenge of training diffusion models to sample from unnormalized energy distributions in the absence of data, the so-called diffusion samplers. Although these approaches have shown promise, they struggle to scale in more…
Studying the effects of air-pollution on health is a key area in environmental epidemiology. An accurate estimation of air-pollution effects requires spatio-temporally resolved datasets of air-pollution, especially, Fine Particulate Matter…
This study evaluates the performance of several machine learning models for predicting hazardous near-Earth objects (NEOs) through a binary classification framework, including data scaling, power transformation, and cross-validation. Six…
We describe state-of-the-art MARCS-code model atmospheres generated for a group of A dwarf, G dwarf, and late-G to mid-K giant standard stars, selected to photometrically calibrate the Spitzer-IRS, and compare the synthetic spectra to…
We aim to deepen our understanding of the M-L transition on planetary-mass companions and isolated brown dwarfs, and search for evidence of possible differences between these two populations of objects. To this end, we present a set of 21…
This article was motivated by the desire to improve Markov chain Monte Carlo methods for spatial survival models in which the locations of individuals in space are known. For a dataset comprising information on n individuals, standard…
This paper presents and discusses an implementation of a multiple target tracking method, which is able to deal with target interactions and prevent tracker failures due to hijacking. The referenced approach uses a Markov Chain Monte Carlo…
In Case-Based Reasoning, when the similarity assumption does not hold, the retrieval of a set of cases structurally similar to the query does not guarantee to get a reusable or revisable solution. Knowledge about the adaptability of…
In this paper, we review the current theory of very low mass stars model atmospheres including the coolest known M~dwarfs, M~subdwarfs, and brown dwarfs, i.e. T$_{eff} \leq 5,000 $K and $-2.0 \le [M/H] \le +0.0$. We discuss ongoing efforts…
Context: Traditional one-dimensional (1D) hydrostatic model atmospheres introduce systematic modelling errors into spectroscopic analyses of FGK-type stars. Aims: We present an updated version of the STAGGER-grid of 3D model atmospheres,…
Markov Chain Monte Carlo (MCMC) algorithms are often used for approximate inference inside learning, but their slow mixing can be difficult to diagnose and the approximations can seriously degrade learning. To alleviate these issues, we…
Ground segmentation is an important preprocessing task for autonomous vehicles (AVs) with 3D LiDARs. To solve the problem of existing ground segmentation methods being very difficult to balance accuracy and computational complexity, a fast…
Despite their large number in the Galaxy, M dwarfs remain elusive objects and the modeling of their photospheres has long remained a challenge (molecular opacities, dust cloud formation). Our objectives are to validate the BT-Settl model…
Many problems require to approximate an expected value by some kind of Monte Carlo (MC) sampling, e.g. molecular dynamics (MD) or simulation of stochastic reaction models (also termed kinetic Monte Carlo (kMC)). Often, we are furthermore…
A large suite of 228 atmospheric retrievals is performed on a curated sample of 19 brown dwarfs spanning the L0 to T8 spectral types using the open-source Helios-r2 retrieval code, which implements the method of short characteristics for…
ML-enabled systems that are deployed in a production environment typically suffer from decaying model prediction quality through concept drift, i.e., a gradual change in the statistical characteristics of a certain real-world domain. To…
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale…
We fit a sample of 49 R=6000 NIR (0.9 - 2.5 micron) T dwarf spectra obtained with Magellan's FIRE spectrograph with two different atmospheric model sets to compare the derived physical parameters such as Teff, log g, cloud opacity, and…
As climate change intensifies, the shift to cleaner energy sources becomes increasingly urgent. With wind energy production set to accelerate, reliable wind probabilistic forecasts are essential to ensure its efficient use. However, since…