Experimental characterization of electron transport and electroluminescence in xenon-molecular mixtures - Henriques, C. A. O.; Fernandes, L. M. P.; Silva, P. A. O. C.; González-Díaz, D.; Azevedo, C. D. R.; Santos, J. M. F. dos; Monteiro, C. M. B. (Frontiers in Detector Science and Technology, 2025)
We have developed a comprehensive methodology to measure electron transport and electroluminescence parameters in xenon-based gaseous detectors using photosensor waveform analysis. Our approach integrates measurements of the Fano factor, electroluminescence fluctuations (Q-factor), scintillation probability, electron drift velocity, diffusion, and attachment coefficients into a unified experimental framework, with particular focus on the effects of molecular additives. Using a driftless Gas Proportional Scintillation Counter and advanced event-depth analysis, we achieved an energy resolution of (7.42 ± 0.02)% FWHM with 5.9-keV X-rays, measured the Fano factor in pure xenon (0.222 ± 0.004), and characterized the impact of CF4, CH4, and CO2 additives on detector performance. Electron transport measurements showed good agreement with Magboltz simulations, validating our methodology. Through Monte Carlo modeling of electron loss mechanisms, we quantified how attachment affects both electroluminescence yield and statistical fluctuations, enabling separation of attachment effects from other yield-degradation mechanisms for accurate determination of scintillation probabilities. For applications requiring optimal position resolution through reduced diffusion, we compared three molecular additives at concentrations providing equivalent electron cloud spread (2.75 mm after 1 m drift): Xe-CF4 (0.015%) maintains near-100% scintillation probability but introduces significant electron attachment affecting energy resolution; Xe-CH4 (0.220%) reduces the scintillation probability by approximately 30% with minimal attachment; while Xe-CO2 (0.041%) combines reduced scintillation, moderate attachment, and VUV opacity. These findings provide a quantitative foundation for selecting optimal additives based on application-specific priorities in rare-event detection experiments.