Support generalized extinction models (G23) by decoupling legacy fAs grid constraints - #867
Open
galaxyumi wants to merge 6 commits into
Open
Support generalized extinction models (G23) by decoupling legacy fAs grid constraints#867galaxyumi wants to merge 6 commits into
G23) by decoupling legacy fAs grid constraints#867galaxyumi wants to merge 6 commits into
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This PR updates the BEAST parameter verification and physics model generation pipeline to accommodate generalized monolithic dust extinction frameworks, specifically
G23model, which are critical for stretching extinction curves reliably into the JWST/NIRCam Long-Wavelength (LW) regimes (>3.33μm).Historically, the codebase tightly coupled all grid validation and matrix calculations to legacy two-component mixture models (which rigidly require an fAs parameter grid triplet and separate ALaw/BLaw sub-classes). This PR breaks those hardcoded assumptions, introducing defensive programming practices that allow the BEAST to dynamically scale between classic mixture models and monolithic/generalized dust laws.
This PR addresses the second part of the Issue #866.