In this paper, we study terahertz generation through optical rectification in reflection at normal incidence in a dielectric nonlinear crystal. We first analyze, with a nonlinear optical model, the sample parameters (thickness, absorption at both laser and terahertz wavelengths, etc.) for which a terahertz optical rectification reflection scheme is preferable to the common transmission scheme. Then, we report our experimental observations of a reflected terahertz signal generated at the surface of a ZnTe crystal. The reflected terahertz signal shares all the characteristics of a signal generated in transmission but is not limited by absorption losses in the crystal, thereby providing a broader bandwidth. At high pump laser power, the signal exhibits saturation, which is caused by the decrease of the nonlinear susceptibility due to photocarriers generated by two-photon absorption. This reflection scheme could be of great importance for terahertz microscopy of opaque materials like, e.g., humid samples or samples exhibiting strong absorption bands or to study samples for which the transmitted signal cannot be recorded.

No abstract

No abstract

The unique properties of terahertz (THz) spectroscopy shows a great potential for security and defense applications such as safe screening of persons and objects. However, a successful implementation of THz screening systems requires a development of reliable and efficient identification algorithms. Dimensionality reduction (DR) methods aim to reduce the dimensionality of the multivariate data and are therefore commonly used as a preprocessing step for classification algorithms and as an analytical tool allowing data visualization. In this paper, we compare the use of unsupervised and supervised DR methods for analysis and classification of THz reflection spectra based on their most widespread linear representatives, namely Principal Component Analysis and Linear Discriminant Analysis, respectively. To this end, both methods were applied to more than 5000 THz reflection spectra acquired from six active materials mixed at three different concentrations with polyethylene and measured at various humidity conditions. While considering scenarios with different level of complexity, we found that supervised approach provide better results because it enables efficient grouping despite intraclass variability. Furthermore, we showed that manipulating labels introduced into the supervised DR algorithm allows conditioning the data for a desired classification task such as security screening. Presented classification results show that simple machine learning algorithms are sufficient for highly accurate classification (>98.6%) of THz spectra, which will be suitable for many real-life applications of THz spectroscopy based on material identification.

No abstract