  Digital Twin is a breaking technology that allows creating virtual
representations of complex physical systems based on updated information of the
system and its physical laws. However, making the Digital Twin behavior
matching with the real system can be challenging due to the number of unknown
parameters in each twin. Its search can be done using optimization-based
techniques, producing a family of models based on different system datasets,
so, a discrimination criterion is required to determine the best Digital Twin
model. This paper presents an information theory-based discrimination criterion
to determine the best Digital Twin model resulting from a behavioral matching
process. The information gain of a model is employed as a discrimination
criterion. Box-Jenkins models are used to define the family of models for each
behavioral matching result. The proposed method is compared with other
information-based metrics as well as the $\nu$gap metric. As a study case, the
discrimination method is applied to the Digital Twin for a real-time vision
feedback infrared temperature uniformity control system. Obtained results show
that information-based methodologies are useful for selecting an accurate
Digital Twin model representing the system among a family of plants
