Engineering topics

Milton, Ryan

Publications and source records attributed to Milton, Ryan.

4 records

DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Comparison of point cloud and image-based models for calorimeter fast simulation

Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Furthermore, point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

A few-degree calorimeter for the future electron-ion collider

In this study, measuring the region 0.1 < Q 2 < 1.0 GeV 2 is essential to support searches for gluon saturation at the future Electron-Ion Collider. Recent studies have revealed that covering this region at the highest beam energies is not feasible with current detector designs, resulting in the so-called Q 2 gap. In this work, we present a design for the Few-Degree Calorimeter (FDC), which addresses this issue. The FDC uses SiPM-on-tile technology with tungsten absorber and covers the range of -4.6 < η < -3.6. It offers fine transverse and longitudinal granularity, along with excellent time resolution, enabling standalone electron tagging. Our design represents the first concrete solution to bridge the Q 2 gap at the EIC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

A high-granularity calorimeter insert based on SiPM-on-tile technology at the future Electron-Ion Collider

Herein we present a design for a high-granularity calorimeter insert for future experiments at the Electron-Ion Collider (EIC). The sampling-calorimeter design uses scintillator tiles read out with silicon photomultipliers. It maximizes coverage close to the beampipe, while solving challenges arising from the beam-crossing angle and mechanical integration. It yields a compensated response that is linear over the energy range of interest for the EIC. Its energy resolution meets the requirements set in the EIC Yellow Report even with a basic reconstruction algorithm. Moreover, this detector will provide 5D shower data (position, energy, and time), which can be exploited with machine-learning techniques. This detector concept has the potential to unleash the power of imaging calorimetry at the EIC to enable measurements at extreme kinematics in electron–proton and electron–nucleus collisions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗