Calo5D

The precise reconstruction of all products of a particle interaction is of critical importance for the discovery reach and the potential for precision measurements of fundamental aspects of the properties and interactions of elementary particles. The detectors at future particle colliders use highly granular “imaging calorimeters” to provide detailed three-dimensional spatial information of the energy deposited by the passage and the showering of particle in the detector. This information is exploited with sophisticated particle flow algorithms, which optimally combine the information available from the different subsystems of the detector. The capability of the calorimeters to detect, separate and measure individual final-state particles is critical. Modern timing devices that achieve precisions of a few 10 to 100 ps turn imaging calorimeters into five-dimensional detectors, covering space, energy and time. This precision is comparable to the calorimeter cell size divided by the speed of light (1 cm = 30 ps × c). To capitalise on these new technologies existing particle flow algorithms will be extended beyond the state of the art, both classically and by integrating machine learning techniques to enable the full exploitation of the added complexity. These algorithms typically use a standardised set of features extracted from the calorimeter information (shower profiles, hit energy distribution etc.). These features will be enhanced by the timing information, owing to the fact that a shower is not a simple cluster, but a time ordered cluster similar to a tree with cell connections slower than light. This process will also inform the requirements for timing elements in calorimeter systems at future e+e–-colliders (Higgs Factories), by combining the developed algorithms with detailed performance studies for key physics channels in the area of Higgs physics and electroweak precision measurements. The CALO5D project will build on the current state of the art of calorimetry and event reconstruction for Higgs factories. The participants of the project are renowned national and international experts for highly granular electromagnetic and hadronic calorimeters and reconstruction techniques including machine learning techniques as well as for physics analysis and phenomenology. For the success of this project, it is essential to put together this expertise in order to estimate reliably the gain from the use of timing in object reconstruction ranging from simple objects such as electrons and photons to complex objects such as jets.