  Physics models typically contain adjustable parameters to reproduce measured
data. While some parameters correspond directly to measured features in the
data, others are unobservable. These unobservables can, in some cases, cause
ambiguities in the extraction of observables from measured data, or lead to
questions on the physical interpretation of fits that require these extra
parameters. We propose a method based on deep learning to extract values of
observables directly from the data without the need for unobservables. The key
to our approach is to label the training data for the deep learning model with
only the observables. After training, the deep learning model can determine the
values of observables from measured data with no ambiguities arising from
unobservables. We demonstrate this method on the phenomenological R-matrix that
is widely utilized in nuclear physics to extract resonance parameters from
cross section data. Our deep learning model based on Transformers successfully
predicts nuclear properties from measurements with no need for the channel
radius and background pole parameters required in traditional R-matrix
analyses. Details and limitations of this method, which may be useful for
studies of a wide range of phenomena, are discussed.
