High-granularity Dual-readout Calorimeter: Evolution of a Classic Prototype

TL;DR

This paper discusses the development of a high-granularity dual-readout calorimeter prototype that improves hadronic shower imaging and energy resolution using advanced segmentation and neural network analysis.

Contribution

It introduces a new high-granularity dual-readout calorimeter with enhanced spatial and timing segmentation, leveraging neural networks for improved energy resolution.

Findings

High granularity improves shower imaging.

Neural networks enhance energy reconstruction.

Prototype design shows promising simulation results.

Abstract

The original dual-readout calorimeter prototype (DREAM), constructed two decades ago, has proven instrumental in advancing our understanding of calorimetry. It has facilitated a multitude of breakthroughs by leveraging signals from complementary media (Cherenkov and scintillation) to capture fluctuations in electromagnetic energy fraction within hadronic showers. Over the years, extensive studies have shed light on the performance characteristics of this module, rendering it exceptionally well-understood. Drawing on this wealth of experience, we have embarked on enhancing the detectors' capabilities further by integrating fast silicon photomultipliers (SiPMs) with finer transverse segmentation, $\sim$1 cm$^2$, as well as longitudinal segmentation by timing measuring better than 10 cm. This configuration will allow us to image hadronic showers with high granularity (HG-DREAM). We argue that the spatial information provided by such a granular detector in a short time window ($\approx$5 ns) leads to substantial enhancement in energy resolution when advanced neural networks are employed in energy reconstruction. We briefly present the current status of work, new concepts that have been introduced to the detector, and expectations from simulations.