相关论文: NeurIPS 2024 Ariel Data Challenge: Characterisatio…
The study of extra-solar planets, or simply, exoplanets, planets outside our own Solar System, is fundamentally a grand quest to understand our place in the Universe. Discoveries in the last two decades have re-defined our understanding of…
Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due…
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…
This is an exciting era for exo-planetary exploration. The recently launched JWST, and other upcoming space missions such as Ariel, Twinkle and ELTs are set to bring fresh insights to the convoluted processes of planetary formation and…
The advancement of technology has led to rampant growth in data collection across almost every field, including astrophysics, with researchers turning to machine learning to process and analyze this data. One prominent example of this data…
The Ariel mission, due to launch in 2029, will obtain spectroscopic information for 1000 exoplanets, providing an unprecedented opportunity for comparative exoplanetology. Retrieval codes - parameteric atmospheric models coupled with an…
Ariel has been selected as the next ESA M4 science mission and it is expected to be launched in 2028. During its 4-year mission, Ariel will observe the atmospheres of a large and diversified population of transiting exoplanets. A key factor…
In 2020 the European Space Agency selected Ariel as the next mission to join the space fleet of observatories to study planets outside our Solar System. Ariel will be devoted to the characterisation of a thousand planetary atmospheres, for…
The Ariel Space Mission aims to observe a diverse sample of exoplanet atmospheres across a wide wavelength range of 0.5 to 7.8 microns. The observations are organized into four Tiers, with Tier 1 being a reconnaissance survey. This Tier is…
The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following…
Since the first discovery of an extra-solar planet around a main-sequence star, in 1995, the number of detected exoplanets has increased enormously. Over the past two decades, observational instruments (both onboard and on ground-based…
Ariel has been selected as ESA's M4 mission for launch in 2028 and is designed for the characterisation of a large and diverse population of exoplanetary atmospheres to provide insights into planetary formation and evolution within our…
The recently adopted Ariel ESA mission will measure the atmospheric composition of a large number of exoplanets. This information will then be used to better constrain planetary bulk compositions. While the connection between the…
Since the discovery of the first extrasolar planet more than twenty years ago, we have discovered nearly four thousand planets orbiting stars other than the Sun. Current observational instruments (on board the Hubble Space Telescope,…
Automatic scheduling techniques are becoming a crucial tool for the efficient planning of large astronomical surveys. A specific scheduling method is being designed and developed for the Atmospheric Remote-sensing Infrared Exoplanet…
The characterisation of exoplanetary systems depends on the accurate determination of host star parameters. The Ariel mission will probe the atmospheres of a statistically significant sample of exoplanets, and so requires a precise…
Characterising the properties of exoplanet atmospheres relies on several interconnected parameters, which makes it difficult to determine them independently. Planetary mass plays a role in determining the scale height of atmospheres,…
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most…
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding. To study their atmospheres, spectroscopic observations are used to infer essential atmospheric…
With the rise of electronic data, particularly Earth observation data, data-based geospatial modelling using machine learning (ML) has gained popularity in environmental research. Accurate geospatial predictions are vital for domain…