A random forest trained on TNG50 internal galaxy properties preclassifies lopsided versus symmetric disk galaxies with about 80% balanced accuracy, and similar accuracy is reached with photometric observables alone.
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A statistical study of lopsided galaxies using random forest
A random forest trained on TNG50 internal galaxy properties preclassifies lopsided versus symmetric disk galaxies with about 80% balanced accuracy, and similar accuracy is reached with photometric observables alone.