cortecs: A Python package for compressing opacities
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914902097002496 |
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| author | Savel, Arjun B. Bedell, Megan Kempton, Eliza M. -R. |
| author_facet | Savel, Arjun B. Bedell, Megan Kempton, Eliza M. -R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07047 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | cortecs: A Python package for compressing opacities Savel, Arjun B. Bedell, Megan Kempton, Eliza M. -R. Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics 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. |
| title | cortecs: A Python package for compressing opacities |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics |
| url | https://arxiv.org/abs/2402.07047 |