  In high-energy physics, precise measurements rely on highly reliable detector
simulations. Traditionally, these simulations involve incorporating experiment
data to model detector responses and fine-tuning them. However, due to the
complexity of the experiment data, tuning the simulation can be challenging.
One crucial aspect for charged particle identification is the measurement of
energy deposition per unit length (referred to as dE/dx). This paper proposes a
data-driven dE/dx simulation method using the Normalizing Flow technique, which
can learn the dE/dx distribution directly from experiment data. By employing
this method, not only can the need for manual tuning of the dE/dx simulation be
eliminated, but also high-precision simulation can be achieved.
