TOAST is a software framework for simulating and processing timestream data collected by telescopes. Telescopes which collect data as timestreams rather than images give us a unique set of analysis challenges. Detector data usually contains noise which is correlated in time as well as sources of correlated signal from the instrument and the environment. Large pieces of data must often be analyzed simultaneously to extract an estimate of the sky signal.
See the full documentation in progress here:
https://hpc4cmb.github.io/toast
Contributors to this project may make use of a variety of tools, including those that leverage AI. These guidelines must be followed:
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A human must submit every pull request and is responsible for communication surrounding the revisions of that pull request until it is merged.
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The human contributor is responsible for ensuring that all code in a pull request does not violate existing licensing and copyright law.
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The human contributor must understand and be able to explain how any submitted code functions.
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Large-scale structural changes will receive extra scrutiny. Design decisions will be consciously chosen to enable the continued ability for humans to understand the codebase.