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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. Statement of needObservations with the latest high-resolution spectrographs (e.g., IGRINS / Gemini South, ESPRESSO / VLT, MAROON-X / Gemini North; Mace et al. ( 2018); Seifahrt et al. (2020); Pepe et al. ( 2021)) have motivated RAM-intensive simulations of exoplanet atmospheres at high spectral resolution.cortecs enables these simulations with more gases and on a broader range of computing architectures by compressing opacity data.
Savel et al. (2024) studied this question.