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The demand for novel detector mediums such as Water-based Liquid Scintillator (WbLS) has increased over the last few decades due to their capability for both low energy particle interactions and higher light yield. Recently, the usage of…
We report on the separation of Cherenkov and scintillation light in BGO and BSO crystals read out with silicon photomultipliers (SiPMs). The two light components are disentangled on an event-by-event basis by combining optical filtering…
A hybrid dual-readout calorimeter concept, comprising both electromagnetic and hadronic sections, has recently been proposed to meet the performance requirements of experiments at future e$^{+}$e$^{-}$ colliders. The front compartment…
High resolution calorimetry with state-of-the-art energy resolution performance for both electromagnetic (EM) and hadronic signals can be achieved using the dual-readout (DR) technique, both in a homogeneous scintillating-crystal…
This research investigates the separation of Cherenkov and Scintillation light signals within a simulated Water-based Liquid Scintillator (WbLS) detector, utilizing the XGBoost machine learning algorithm. The simulation data were gathered…
Hadronic calorimeters with dual readout measure both scintillation and Cherenkov lights produced in their active media. They offer improvements in energy resolution and, therefore, have become increasingly interesting due to the need for…
In this paper, we describe the first tests of a dual-readout fiber calorimeter in which silicon photomultipliers are used to sense the (scintillation and Cherenkov) light signals. The main challenge in this detector is implementing a design…
We propose extending parallel fiber dual readout calorimetry to tiles, more applicable to many future experimental requirements, with superior energy resolution. Monte Carlo (MC) studies indicate that a tile dual calorimeter including an…
The RD52 (DREAM) collaboration is performing R\&D on dual readout calorimetry techniques with the aim of improving hadronic energy resolution for future high energy physics experiments. The simultaneous detection of Cherenkov and…
Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles…
Large liquid-scintillator-based detectors have proven to be exceptionally effective for low energy neutrino measurements due to their good energy resolution and scalability to large volumes. The addition of directional information using…
We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent…
Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular,…
Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leading to substantial improvements in diagnostic capability for diseases such as cancer and…
The ability to separately identify the Cherenkov and scintillation light components produced in scintillating mediums holds the potential for a major breakthrough in neutrino detection technology, allowing development of a large,…
The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling…
Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
In this White Paper for the 2021 Snowmass process, we detail the status and prospects for dual-readout calorimetry. While all calorimeters allow estimation of energy depositions in their active material, dual-readout calorimeters aim to…
Future lepton colliders such as the FCC-ee, CEPC, ILC, or a muon collider will collect large data samples that allow precision physics studies with unprecedented accuracy, especially when the data is collected by innovative state-of-the-art…