  Cluster counting in drift chamber is the most promising breakthrough in
particle identification (PID) technique in particle physics experiment.
Reconstruction algorithm is one of the key challenges in cluster counting. In
this paper, a semi-supervised domain adaptation (DA) algorithm is developed and
applied on the peak finding problem in cluster counting. The algorithm uses
optimal transport (OT), which provides geometric metric between distributions,
to align the samples between the source (simulation) and target (data) samples,
and performs semi-supervised learning with the samples in target domain that
are partially labeled with the continuous wavelet transform (CWT) algorithm.
The model is validated by the pseudo data with labels, which achieves
performance close to the fully supervised model. When applying the algorithm on
real experimental data, taken at CERN with a 180 GeV/c muon beam, it shows
better classification power than the traditional derivative-based algorithm,
and the performance is stable for experimental data samples across varying
track lengths.
