Abstract
Noble liquid calorimetry is a well proven technology that successfully operated in numerous particle physics detectors (D0, H1, NA48, NA62, ATLAS, …). Its excellent linearity, stability, uniformity and radiation hardness as well as very good energy and time resolution make it a strong candidate for future hadron and lepton colliders. Recently, a highly granular noble liquid sampling calorimeter was proposed for a possible FCC-hh experiment. It has been shown that, on top of its intrinsic excellent conventional calorimetry performance, this technology can be optimized in terms of granularity to allow for 4D imaging, machine learning and – in combination with the tracker measurements – particle-flow reconstruction. This paper discusses how such a detector can be adapted to an electron–positron collider (FCC-ee) experiment, shows that a signal over noise ratio above five can be reached while keeping the cross-talk at the percent level, presents a possible signal extraction scheme and reports on the first performance studies derived with the Key4hep full simulation framework.
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