Speaker
Description
XENONnT is a dual-phase time projection chamber with a target of 6 tonnes of ultra-pure liquid xenon which aims to search for dark matter via its scattering process. In XENONnT, incoming particles scatter and excite the target xenon atoms, resulting in an initial scintillation signal and an ionization reaction. The freed electrons from the ionization cause a second, larger ionization signal at the liquid-gas interface. Both of these signals are measured by arrays of photosensors. These photosensor measurements allow energy reconstruction of the incoming particle, which is crucial for particle identification and energy resolution in rare event searches. In this talk, I will discuss the potential use of machine-learning techniques to infer the energy of the incoming particle and to improve upon the current energy resolution set by standard calorimetry techniques.
| Consider for long presentation | No |
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