n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022

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license: mit
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# **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

原始信息汇总

Ariel Data Challenge NeurIPS 2022 数据集概述

数据集来源

• 数据集属于 Ariel Machine Learning Data Challenge。
• Ariel Space 任务是由欧洲空间局发起，计划于2029年发射。

数据集目的

• 通过观察1000颗系外行星的大气，研究它们的形成、演化以及将我们的太阳系置于银河系背景中的位置。
• 理解银河系中大约5000颗已知系外行星的形成和演化历史，特别是通过分析它们大气的化学成分、温度、云覆盖等。

数据集应用

• 加速模型拟合过程，当前使用MCMC方法进行大气模型拟合，但当大气正向模型运行速度慢时，收敛问题变得困难。
• 利用机器学习进行逆向建模，以改进模型拟合效率。

支持与合作

• 感谢 NeurIPS 2022 主办数据挑战。
• 感谢 UK Space Agency 和 European Research Council 的支持。
• 感谢数据挑战团队、合作研究所及 Ariel 团队的技术支持。

联系方式

• 更多信息请联系：exoai.ucl [at] gmail.com