Speaker
Description
The Quadruple Gas Electron Multiplier (QGEM) detectors, employing cascaded GEM foils, achieves high effective gain while reducing the risk of electrical discharges due to operation at relatively low voltage. The resulting high gain improves the signal to noise ratio, enabling efficient detection of even small ionization events. This structure also suppresses ion backflow into the drift volume, preventing space charge buildup that can distort particle tracks and degrade position resolution. In addition, it reduces gain fluctuations, leading to a more precise measurement of particle energy deposition hence improved energy resolution. Due to these advantages, QGEMs are utilized in high rate environments like in ALICE Time Projection Chambers. They employ GEMs with 140 µm and 280 µm hole pitches. However, systematic studies covering a broader range of pitches remain limited. Optimization of pitch is important because it directly affects electron transport, gain uniformity, ion backflow, and overall detector performance. In our previous study on single GEM, we demonstrated that reduced pitch (90 and 60 µm) improves effective gain, collection efficiency, and spatial confinement of electron clouds. In subsequent work on varied pitch triple GEMs, we examined how geometrical parameters, gas compositions, and operating voltages influence electron transport and diffusion. In the present study, we extend this approach to a QGEM detector. Using an ANSYS and Garfield++ framework validated against experimental effective gain and ion backflow fraction (IBF) measurements, we analyze QGEM performance across 70–280 µm hole pitches. The geometrical ratio is fixed at 14:7:5 for pitch, outer hole diameter, and inner hole diameter to preserve proportional scaling. The results highlight the sensitivity of electron transmission and ion flowback resulting from geometrical scaling, pitch variation, and electric-field settings, showing their combined effect on the balance between effective gain and IBF. These simulation findings provide guidance for optimizing QGEM performance.
| Name of the speaker | Ajay Kumar |
|---|---|
| Eligible for the Georges Charpak Young Scientist Award. | no |