Related papers: Optimizing exoplanet atmosphere retrieval using un…
Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satellite observations to understand cloud feedbacks in a warming…
We propose a novel unsupervised outlier detection method based on Randomized Principal Component Analysis (PCA). Motivated by the performance of Randomized PCA (RPCA) Forest in approximate K-Nearest Neighbor (KNN) search, we develop a novel…
The large number of new planets expected from wide-area transit surveys means that follow-up transmission spectroscopy studies of their atmospheres will be limited by the availability of telescope assets. We argue that telescopes covering a…
In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data from solar active regions, recorded by solar imaging…
This chapter reviews the current state of observational and theoretical efforts in the characterization of exoplanet atmospheres, with a focus on developments enabled through the Swiss National Centre for Competence in Research (NCCR)…
Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and…
We outline how principal component analysis (PCA) can be applied to particle configuration data to detect a variety of phase transitions in off-lattice systems, both in and out of equilibrium. Specifically, we discuss its application to…
High-resolution Doppler spectroscopy is a powerful tool for identifying molecular species in the atmospheres of both transiting and non-transiting exoplanets. Currently, such data is analysed using cross-correlation techniques to detect the…
This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from…
Contemporary exoplanet and brown dwarf atmospheric research relies heavily on retrieval frameworks to recover thermal and chemical properties and perform model comparison in an observational data-driven approach. However, the computational…
State of the art spectral retrieval models of exoplanet atmospheres assume constant chemical profiles with altitude. This assumption is justified by the information content of current datasets which do not allow, in most cases, for the…
Transmission spectroscopy is a powerful technique widely used to probe exoplanet terminators. Atmospheric retrievals of transmission spectra are enabling comparative studies of exoplanet atmospheres. However, the atmospheric properties…
Transmission spectroscopy is one of the most successful methods of learning about exoplanet atmospheres. The process of retrievals using transmission spectroscopy consists of creating numerous forward models and comparing them to…
Machine learning (ML) methods have proved to be a very successful tool in physical sciences, especially when applied to experimental data analysis. Artificial intelligence is particularly good at recognizing patterns in high dimensional…
Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical…
Atmospheric characterisation of exoplanets from the ground is an actively growing field of research. In this context we have created the ATMOSPHERIX consortium: a research project aimed at characterizing exoplanets atmospheres using…
Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly severe when only a…
Principal Component Analysis (PCA) has been widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA), under different robust distance metrics, such as l1-norm and l2, p-norm, can deal with noise or outliers to some…
This tutorial is an introduction to techniques used to characterize the atmospheres of transiting exoplanets. We intend it to be a useful guide for the undergraduate, graduate student, or postdoctoral scholar who wants to begin research in…
Certain cancer types, notably pancreatic cancer, are difficult to detect at an early stage, motivating robust biomarker-based screening. Liquid biopsies enable non-invasive monitoring of circulating biomarkers, but typical machine learning…