标题： PyATMOS：可扩展的假想行星大气网格 显示英文标题

标题： PyATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres

摘要： 云计算提供了一个机会，通过并行化模型运行来高效地生成对系外行星社区有用的大型数据集，从而运行计算资源密集型的气候模型。 为了更好地理解潜在宜居行星大气的统计分布和特性，我们实现了一种并行化的气候建模工具，以扫描一系列假设的大气环境。从现代地球大气开始，我们迭代地模拟了一系列大气环境，以推断多参数空间的景观，例如由生物介导气体（\ce{O2}、\ce{CO2}、\ce{H2O}、\ce{CH4}、\ce{H2}和\ce{N2}）组成的丰度，这些气体将在类似地球的行星上围绕类太阳恒星产生“稳态”行星大气。 我们当前的数据集包括了 \numatmospheres 个系外行星大气的模拟模型，并且可以在NASA系外行星档案库中公开获取。 我们可扩展的大气分析方法还可以通过提供作为高度函数的大气气体通量和温度估计，帮助解释未来对行星大气的观测。这些数据可以实现对系外行星表面潜在宜居性的高吞吐量初步评估，并且可以成为大气科学和系外行星科学领域机器学习应用的学习数据集。 显示英文摘要

摘要： 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 statistical distributions and properties of potentially habitable planetary atmospheres we implemented a parallelised climate modelling tool to scan a range of hypothetical atmospheres.Starting with a modern day Earth atmosphere, we iteratively and incrementally simulated a range of atmospheres to infer the landscape of the multi-parameter space, such as the abundances of biological mediated gases (\ce{O2}, \ce{CO2}, \ce{H2O}, \ce{CH4}, \ce{H2}, and \ce{N2}) that would yield `steady state' planetary atmospheres on Earth-like planets around solar-type stars. Our current datasets comprises of \numatmospheres simulated models of exoplanet atmospheres and is available publicly on the NASA Exoplanet Archive. Our scalable approach of analysing atmospheres could also help interpret future observations of planetary atmospheres by providing estimates of atmospheric gas fluxes and temperatures as a function of altitude. Such data could enable high-throughput first-order assessment of the potential habitability of exoplanetary surfaces and sepcan be a learning dataset for machine learning applications in the atmospheric and exoplanet science domain.