  Pattern recognition and machine learning techniques are known to play an
emerging role for studying underlying physics of light-matter interaction.
While on the subject, photon condensation and Anti-Stokes cooling remain
compelling phenomena of laser-underdense plasma interaction. In this work, the
interaction of a 1064 nm continuum-wave laser with inductively-coupled plasma
(ICP) of Mercury(Hg) has been studied by probabilistic pattern recognition over
observed time resolved spectral data.3D vector fields obtained by the
probabilistic pattern recognition over spectral database proficiently
illuminates the analogue black hole (ABH) and its sonic horizon in which
homoclinic orbit Whistler waves are exited. Vector spectra show the collective
behavior of condensation and anti-Stokes cooling is due to the electronic
transitions of Hg1 trapped by the phonon sink of ABH of laser. Modeling of
subsonic phonon vector spectrum obtained by the probabilistic linear
discriminant analysis(PLDA) estimates the 0.70 nanoKelvin of temperature of the
sink region of ABH.
