arXiv subjects

M. F. Yilmaz

Publications and source records attributed to M. F. Yilmaz.

4 recordsLinked to original sources

arXiv2020

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.

physics.plasm-ph↗

arXiv2020

A hybrid machine learning model to study UV-Vis spectra of gold nano spheres

Here, we have employed Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to analyze Mie calculated UV-Vis spectra of gold nanospheres (GNS). Eigen spectra of PCA perform the Fano type resonances.3D vector field spectra reveal the Homoclinic orbit Lorenz attractor. Quantum confinement effects are observed by 3D representation of LDA. Standing wave patterns resulting from oscillations of ion acoustic phonon and electron waves are illustrated through the eigen spectra of LDA. Such capabilities of GNPs have brought high attention for the high energy density physics applications. Furthermore, accurate prediction of gold nanoparticle (GNP) sizes using machine learning could provide rapid analysis without the need for expensive analysis. Two hybrid algorithms consist of unsupervised PCA and two different supervised ANN have been used to estimate the diameters of GNPs. PCA based artificial neural network (ANN) were found to estimate the diameters with a high accuracy.

physics.comp-ph↗

arXiv2018

Testing the Use of the Principal Component Analysis Method to Detect Slight Changes in Nitrogen and Oxygen Spectra Induced by a Change in Discharge Conditions

In this work, the influence of crossed magnetic fields (B=100 Gauss) on the UV-vis and NIR spectra and breakdown voltages of nitrogen and oxygen at a pair of parallel plane copper electrodes with a spacing d=2 cm and diameter R= 2.2 cm are studied. Working pressures of the gases are kept between 0.1 and 1 Torr. The breakdown voltage measurements across the electrodes are conducted by Tektronix P6015 high voltage probe connected to a Tektronix 2430A oscilloscope. The Spectra of discharge plasmas in the absence and presence of magnetic fields are recorded between 200-1100 nm. by AvaSpec-ULS3648. In order to analyse observed spectra of nitrogen and oxygen plasmas, one of the pattern recognition techniques of principal component analysis has been employed. Results of principal component analysis shows that the presence of magnetic field cause the plasma particles to move in condensed way for the oxygen but not for the nitrogen. Principal component spectra shows that small amount of the cross magnetic field results in the Stokes linear polarization of the most of the oxygen and NIII (337 nm) of the Nitrogen species in the plasmas. The non-local thermal equilibrium based spectroscopic modeling of nitrogen and oxygen plasma gives that the electron temperature of nitrogen and oxygen are Te=25000 and 5000 Kelvin respectively.

physics.plasm-ph↗

arXiv2018

Investigation of electron beam effects on L shell Mo plasma produced by a compact LC generator using pattern recognition

In this paper, the effects of electron beam on the X pinch produced spectra of L-shell Mo plasma have been investigated by principal component analysis (PCA), and this analysis is compared with that of line ratio diagnostics. Spectral database for PCA extraction was arranged using the non-LTE collisional radiative L-shell Mo model. PC vector spectra of L shell Mo including F, Ne, Na and Mg- like transitions were studied to investigate the polarization types of these transitions. PC1 vector spectra of F, Ne, Na and Mg- like transitions resulted in linear polarization of Stokes Q profiles. Besides, PC2 vector spectra showed linear polarization of Stokes U profiles of 2p53s of Ne like transitions which were recognized as responsive to the magnetic field (Trabert et al., 2017). 3D representation of PCA coefficients demonstrated that addition of electron beam to the non-LTE model generates the quantized collective clusters which are translations of each other and follow V-shaped cascade trajectories except for the case f = 0.0. The extracted principal coefficients were used as a database for the Artifical Neural Network (ANN) to estimate the plasma electron temperature, density and beam fractions of time integrated spatially resolved L-shell Mo X-pinch plasma spectrum. PCA based ANN provides advantage in reducing the network topology with a more efficient Backpropagation supervised learning algorithm. The modeled plasma electron temperature is about Te~660 eV and density ne=1x1020 cm-3 in the presence of the fraction of the beams with f~0.1 and centered energy of 5 keV.

physics.plasm-ph↗