  Substantial developments in pattern recognition and machine learning offer
unique advancements to study the deeper structures of plasma physics. We have
employed the Kernel principal component analysis(KPCA) of Argon glow discharge
plasmas over time-resolved UV-Vis spectra for different pressure scans. 3D
vector fields obtained by KPCA reveal the chaotic structure, strange
attractors. The presence of the cross magnetic field (B=100G) and the addition
of pressure increase the degree of chaos. Lower scale vector fields show that
phonon sinks and traps and cool the plasma electron temperatures. The average
electron and ion density profiles are provided by the 2D particle in cell
simulation. Welch's transformation of the lower to higher scale vector fields
illuminates the symmetric zero-point energy fluctuations of particles,
antiparticles, and virtual particles. These positive and negative energies
cancel each other and the combination of these energy fields form another type
of fractal structure.
