
# **Ariel Data Challenge NeurIPS 2022**

Dataset is part of the [**Ariel Machine Learning Data Challenge**](https://www.ariel-datachallenge.space/). The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context. 

### **Understanding worlds in our Milky Way**

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help! 

### **Speed up model fitting!**

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation.

### **Many thanks to...**

[NeurIPS 2022](https://nips.cc/) for hosting the data challenge and to the [UK Space Agency](https://www.gov.uk/government/organisations/uk-space-agency) and the [European Research Council](https://erc.europa.eu/) for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the [Ariel](https://arielmission.space/) team for technical support and building the space mission in the first place!

For more information, contact us at: exoai.ucl [at] gmail.com

license:mit region:us
{"license": "mit"}
TAGS
#license-mit #region-us 


# Ariel Data Challenge NeurIPS 2022

Dataset is part of the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context. 

### Understanding worlds in our Milky Way

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help! 

### Speed up model fitting!

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation.

### Many thanks to...

NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the Ariel team for technical support and building the space mission in the first place!

For more information, contact us at: URL [at] URL

# Ariel Data Challenge NeurIPS 2022

Dataset is part of the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context. ### Understanding worlds in our Milky Way

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help! ### Speed up model fitting!

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation. ### Many thanks to...

NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the Ariel team for technical support and building the space mission in the first place!

For more information, contact us at: URL [at] URL
TAGS
#license-mit #region-us 
 # Ariel Data Challenge NeurIPS 2022

Dataset is part of the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context. ### Understanding worlds in our Milky Way

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help! ### Speed up model fitting!

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation. ### Many thanks to...

NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the Ariel team for technical support and building the space mission in the first place!

For more information, contact us at: URL [at] URL
passage: TAGS
#license-mit #region-us 
# Ariel Data Challenge NeurIPS 2022

Dataset is part of the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context.### Understanding worlds in our Milky Way

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help!### Speed up model fitting!

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation.### Many thanks to...

NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the Ariel team for technical support and building the space mission in the first place!

For more information, contact us at: URL [at] URL