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A fast optical spectroscopic method for determination rotational ($T_{rot}$)and vibrational ($T_{vib}$) temperatures in two-temperature Boltzmanndistribution of the excited state by using machine learning approach ispresented. The method is applied to estimate molecular gas temperatures in adirect current glow discharge in hydrogen-methane gas mixture duringplasma-enhanced chemical vapor deposition of carbon film materials. Slightlyresolved ro-vibrational optical emission spectrum of the $C_2$ ($\nu'=0 \to\nu''=0$) Swan band system was used for local temperature measurements inplasma ball. Random Forest algorithm of machine learning was explored fordetermination of temperature distribution maps. In addition to the $T_{rot}$ ,$T_{vib}$ maps, distribution maps and their gradients for electron temperature($T_e$) and for the emission intensity of the spectral line 516,5$nm$corresponding to $C_2$ species is presented and is discussed in detail.

Processing slightly resolved ro-vibrational spectra during chemical  vapor deposition of carbon materials: machine learning approach for plasma  thermometry