Probabilistic principal component and linear discriminant analysis of interaction of 1064 nm CW laser with ICP plasma: Anti-Stokes cooling due to analogue black hole.

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.