Abstract
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and detector effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size, and the total energy loss within each segment is used as the signal. As an approximation of an optimal reconstruction, a neural network-based energy regression is trained based on these signals. The architecture is based on blocks of convolutional kernels customized for shower energy regression using local energy densities; biases at the edges of the training dataset are mitigated using a histogram technique. With this approximation, we find that a longitudinal and transverse segment size less than or equal to 0.5 and 1.3 nuclear interaction lengths, respectively, is necessary to achieve an optimal energy measurement. In addition, an intrinsic energy resolution of 8%/sqrt{E} for pion showers is observed.
Highlights
Many hadronic calorimeters currently in use and planned for future experiments are sampling calorimeters, which consist of alternating active and passive absorber layers [10,11,12,13]
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers
To factorize out sampling and detector effects, events are simulated in which a single charged pion is shot at a homogenous lead glass calorimeter, split into longitudinal and transverse segments of varying size, and the total energy loss within each segment is used as the signal
Summary
Many hadronic calorimeters currently in use and planned for future experiments are sampling calorimeters, which consist of alternating active and passive absorber layers [10,11,12,13]. The e/ h ratio has been adjusted closer to 1 by either suppressing the electromagnetic response, e.g., by using highZ absorbers, or by enhancing the hadronic response, using neutron-sensitive active materials. Calorimeters that have a ratio e/ h ∼ 1 are called “compensating” calorimeters. These optimizations in the active and passive materials often require a decreased sampling fraction (ratio of active/passive material), which itself degrades the calorimeter energy resolution by increasing the stochastic term α of σE = √α ⊕ c. The stochastic term is dominated by the sampling fraction (per layer) and the frequency (the number of layers) for sampling calorimeters, and expresses the dependence of the
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