cortecs：用于压缩不透明度的 Python 软件包
cortecs: A Python package for compressing opacities
AI总结：
本文介绍Python工具cortecs，通过灵活拟合温度、压力和波长依赖关系并采用GPU友好方法压缩系外行星大气不透明度数据，解决高分辨率宽波长模拟中内存占用过大的问题。
摘要：
系外行星大气对光的吸收和发射编码了大气成分、温度和动力学的详细信息。从根本上说，模拟这些过程需要详细了解大气中气体的不透明度。当以高分辨率模拟宽波长范围时，即使是单一气体的不透明度数据也可能占用数GB的系统随机存取存储器（RAM）。在考虑模拟中包含的气体数量、用于推断的采样策略，甚至用于计算的系统架构时，这一方面可能成为一个限制因素。在此，我们介绍cortecs，一个用于压缩不透明度数据的Python工具。cortecs提供了灵活的方法来拟合不透明度数据的温度、压力和波长依赖性，并使用加速的、GPU友好的方法来评估不透明度。该软件包正在GitHub上积极开发（https://github.com/arjunsavel/cortecs），并可通过pip和conda下载。
英文摘要：
The absorption and emission of light by exoplanet atmospheres encode details of atmospheric composition, temperature, and dynamics. Fundamentally, simulating these processes requires detailed knowledge of the opacity of gases within an atmosphere. When modeling broad wavelength ranges at high resolution, such opacity data, for even a single gas, can take up multiple gigabytes of system random-access memory (RAM). This aspect can be a limiting factor when considering the number of gases to include in a simulation, the sampling strategy used for inference, or even the architecture of the system used for calculations. Here, we present cortecs, a Python tool for compressing opacity data. cortecs provides flexible methods for fitting the temperature, pressure, and wavelength dependencies of opacity data and for evaluating the opacity with accelerated, GPU-friendly methods. The package is actively developed on GitHub (https://github.com/arjunsavel/cortecs), and it is available for download with pip and conda.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
