Speaker
Description
The physics program of future Higgs factories requires unprecedented jet energy resolution to efficiently discriminate among hadronic boson decays. Particle Flow Calorimetry provides a powerful reconstruction paradigm to achieve this objective; however, it places stringent demands on detector technologies, particularly in terms of granularity, to resolve individual particles within jets and reconstruct their kinematics with optimal energy/momentum resolution.
In this context, resistive Micro-Pattern Gaseous Detectors (MPGDs) are identified as a promising solution for the active layers of sampling hadronic calorimeters. Technologies such as resistive Micromegas and µ-RWELL combine high-granularity readout with intrinsic discharge mitigation, delivering excellent spatial (O(mm)) and timing (O(10ns)) resolution, as well as stable and uniform responses. Furthermore, their capability to operate at particle rates up to 10 MHz/cm² makes them well-suited for high-background environments.
Within this framework, we present the R&D activities toward a novel MPGD-based HCAL employing a semi-digital readout (SDRO), with the additional goal of exploiting timing information. A comprehensive performance optimization strategy is developed, combining detailed hardware characterization with simulation studies. In particular, machine learning techniques are employed to enhance the SDRO-based energy reconstruction, demonstrating improved performance in GEANT4 simulations.
Complementary beam tests with muons were conducted at the CERN SPS to evaluate the efficiency, response uniformity, and time resolution of individual detector layers. Finally, the validation of the detector concept is pursued through the construction and testing of a small-scale HCAL prototype, consisting of alternating iron absorbers and MPGD layers, with its energy response studied using pion beams up to approximately 10 GeV.