  Jet substructure variables aim to reveal details of the parton fragmentation
and hadronization processes that create a jet. By removing collinear radiation
while maintaining the soft radiation components, one can construct
CollinearDrop jet observables, which have enhanced sensitivity to the soft
phase space within jets. We present a CollinearDrop jet measurement, corrected
for detector effects with a machine learning method, MultiFold, and its
correlation with groomed jet observables, in $pp$ collisions at $\sqrt{s}=200$
GeV at STAR. We demonstrate that the population of jets with a large
non-perturbative contribution can be significantly enhanced by selecting on
higher CollinearDrop jet mass fractions. In addition, we observe an
anti-correlation between the amount of grooming and the angular scale of the
first hard splitting of the jet.
