  The problems of heating and acceleration of solar wind particles are of
significant and enduring interest in astrophysics. The interactions between
waves and particles are crucial in determining the distributions of proton and
alpha particles, resulting in non-Maxwellian characteristics including
temperature anisotropies and particle beams. These processes can be better
understood as long as the beam can be separated from the core for the two major
components of the solar wind. We utilized an alternative numerical approach
that leverages the clustering technique employed in Machine Learning to
differentiate the primary populations within the velocity distribution, rather
than employing the conventional bi-Maxwellian fitting method. Separation of the
core and beam revealed new features for protons and alphas. We estimated that
the total temperature of the two beams was slightly higher than that of their
respective cores, and the temperature anisotropy for the cores and beams was
larger than 1. We concluded that the temperature ratio between alphas and
protons largely over 4 is due to the presence of a massive alpha beam, which is
approximately 50\% of the alpha core. We provided evidence that the alpha core
and beam populations are sensitive to Alfv\'enic fluctuations and the surfing
effect found in the literature can be recovered only when considering the core
and beam as a single population. Several similarities between proton and alpha
beams would suggest a common and local generation mechanism not shared with the
alpha core, which may not have necessarily been accelerated and heated locally.
