  Continuous wavelet analysis is gaining popularity in science and engineering
for its ability to analyze data across spatial and scale domain simultaneously.
In this study, we introduce a wavelet-based method to identify halos and assess
its feasibility in two-dimensional (2D) scenarios. We begin with the generation
of four pseudo-2D datasets from the SIMBA dark matter simulation by compressing
thin slices of three-dimensional (3D) data into 2D. We then calculate the
continuous wavelet transform (CWT) directly from the particle distributions,
identify local maxima that represent actual halos, and segment the CWT to
delineate halo boundaries. A comparison with the traditional Friends-of-Friends
(FOF) method shows that our CWT-identified halos, while containing slightly
fewer particles, have smoother boundaries and are more compact in dense
regions. In contrast, the CWT method can link particles over greater distances
to form halos in sparse regions due to its spatial segmentation scheme. The
spatial distribution and halo power spectrum of both CWT and FOF halos
demonstrate substantial consistency, validating the 2D applicability of CWT for
halo detection. Our identification scheme operates with a linear time
complexity of $\mathcal{O}(N)$, suggesting its suitability for analyzing
significantly larger datasets in the future.
