相关论文: Exoplanetary atmospheres retrieval via a quantum e…

Cloud computing offers an opportunity to run compute-resource intensive climate models at scale by parallelising model runs such that datasets useful to the exoplanet community can be produced efficiently. To better understand the…

The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use…

The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This…

Climate change and its impact on global sustainability are critical challenges, demanding innovative solutions that combine cutting-edge technologies and scientific insights. Quantum machine learning (QML) has emerged as a promising…

Exoplanet atmospheric modeling is advancing from chemically diverse one-dimensional (1D) models to three-dimensional (3D) global circulation models (GCMs), which are crucial for interpreting observations from facilities like the James Webb…

Thanks to the advances in modern instrumentation we have learned about many exoplanets that span a wide range of masses and composition. Studying their atmospheres provides insight into planetary origin, evolution, dynamics, and…

The high-precision photometry from NASA's Kepler and TESS missions has revolutionized exoplanet detection, enabling the discovery of over 5500 confirmed exoplanets via the transit method and around 10000 additional candidates awaiting…

Atmospheric retrievals are now a standard tool to analyze observations of exoplanet atmospheres. This data-driven approach quantitatively compares atmospheric models to observations in order to estimate atmospheric properties and their…

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…

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…

Direct imaging of widely separated exoplanets from space will obtain their reflected light spectra and measure their atmospheric properties, and small and temperate planets will be the focus of the next generation telescopes. In this work,…

Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to…

This paper explores the potential contribution of quantum computing, specifically the Variational Quantum Eigensolver (VQE), into atmospheric physics research and application problems using as an example the Lorenz system, a paradigm of…

Detecting and characterising exoplanet atmospheres remains challenging because atmospheric signals can be comparable to residual noise and instrumental/astrophysical systematics. Spectral features span from a few ppm for small planets up to…

The best-characterized exoplanets to date are planets on close-in transiting orbits around their host stars. The high level of irradiation and transiting geometry of these objects make them ideal targets for atmospheric investigations.…

We introduce Extreme Quantum Cognition Machines, a class of quantum learning architectures for deliberative decision making that is tolerant to noisy and contradictory training data. Inspired by the quantum cognition paradigm, Extreme…

Spectroscopic observations of exoplanet atmospheres can reveal the chemical composition, temperature, cloud properties, and (potentially) the habitability of these distant worlds. The inference of such properties is generally enabled by…

In the recent years, retrieval analysis of exoplanet atmospheres have been very successful, providing deep insights on the composition and the temperature structure of these worlds via the transit and eclipse methods. Analysis of spectral…

We are delighted to see the recent development of physics-informed extreme learning machine (PIELM) for its higher computational efficiency and accuracy compared to other physics-informed machine learning (PIML) paradigms. Since a…

The discovery of extrasolar planets is one of the major scientific advances of the last two decades. Hundreds of planets have now been detected and astronomers are beginning to characterise their composition and physical characteristics. To…